Claude runs my Substack growth now. For a long time, the thing slowing it down was me.

Not the strategy. Not the tools. Me.

I knew what to do to grow on Substack. I just could not keep up with doing it.

In the last six months, I moved houses twice. Twice.

Every writing cadence I had set, every posting schedule, every plan, I dropped most of them because I was living out of boxes.

On top of that: a full-time job and two little kids. There is not much spare time in that, and there is even less spare energy.

I kept coming back to the Pareto principle, the old 80/20 rule. Most of a job is the mechanical, repeatable, trackable part, call it 80%. The meaningful part, the judgment and the voice, is the 20% left over.

So I started asking simple question about Substack: what can I hand off, and what do I want to keep for myself? The answer turned into eight workflows.

Gradually, I took the parts of Substack growth I cannot realistically keep track of, and handed them to Claude. And only kept the parts I enjoy.

Of course, Claude did a much better job than I could (before March).

All Claude handled were just eight workflows that took over the 80% I used to grind through by hand. This session will break down all of them.

What’s inside:

• The 3 stages everyone on Substack moves through

• The one MCP that runs all eight workflows (it is already included in your subscription)

• The full live build, all eight workflows in Claude:

  • How to turn your entire subscription inbox into one scannable read

  • How to size a niche before you spend a month on it

  • How to tear down exactly how a top creator wins: their titles, their SEO, their cadence

  • How to turn one post into a week of notes in your own voice

  • How to schedule a week of notes and walk away

  • How to find the utility winner, the note that quietly did the work

  • How to rank your posts on real conversion and pull your next article ideas

  • How to read the subscribers you already have and see who actually converts

  • The complete loop, where measurement feeds the next week of writing

  • Live from the session: the workflow that broke on camera, how I debugged it, and what the room is using Substack for

• What to do this week, in order

• Session timeline with timestamps

🎁 The full recording, the deck, and all eight workflow prompts are in the Build to Launch community portal.

The Three Reasons You’re on Substack

I opened the session with one question in the chat: what are you using Substack for?

One, to learn and read. Two, to build your own publication. Three, to grow toward monetization.

Four people answered, and I got four different stages.

One was mostly here to learn. One was building his own publication. One named brand, one picked all three at once.

That spread is the point. Most of us do not live in one box. You start by reading, you drift into building your own voice, and eventually you want the work to pay you back.

The engine has three stages because the path has three stages, and it meets you at whichever one you are standing in.

This is the same arc I laid out in everything I have learned about growing on Substack. The difference now is that the manual clicking runs as workflows instead.

What I Hand to Claude

Across those three stages, I run eight workflows. Here is the whole set, in the order I reach for them.

1. Subscription Brief turns my entire subscription inbox into one short, themed read. Reach for it when you follow more than you can keep up with and want the two worth opening today.

   

2. Landscape Explorer sizes a niche before I commit a month of my life to it. It is for weighing a topic or a pivot: how crowded it is, whether anyone pays, before you write a word.

   

3. Creator Study tears down exactly how a top creator wins. Use it when someone in your lane is clearly working and you want their real playbook: their cadence, their titles, what they gate.

   

4. Notes Engine turns one long post into a batch of notes in my voice. This is daily visibility without daily writing: one article becomes a week of notes.

5. Scheduler queues those notes so I write once and walk away. It is for when you can write but cannot keep showing up.

6. Notes Performance tells me which notes worked, and which quietly did the work. Reach for it when likes are lying and you want the note that pulled clicks and subscribers.

7. Posts Performance ranks my articles on real conversion and hands me what to write next. Good for the blank page: your next ideas, each traced to a post that already converted.

8. Subscriber Study reads the readers I already have and shows who converts. Use it when you are chasing new subscribers and forgetting the list you already own.

You do not need all eight to start. You need the first one today, and the rest as each stage becomes practical for you.

The full live build

Here is what we built, step by step.

Read more

Ten minutes. Claude created a landing page, hooked up payments, and made it a live URL. I didn’t open a single dashboard.

That happened at the end of this live session, and it is the most appreciated moment I have of what a fully connected Claude can do.

In previous sessions, we’ve used Claude as a thinking partner, running dialogues that surface your own edge.

Useful. But every session ended the same way.

Claude had the plan. I was the one clicking buttons, switching tabs, copy-pasting outputs into other tools. The insights stayed in the chat. The execution stayed with me.

This session completely changed that.

What’s inside:

• How AI use has advanced

• Fix this old habit to be ready for agentic AI

• MCP work has 3 types

• 3 workflow levels, live:

  • VOC research

  • Visual design

  • Live product page build → deploy in 10 min

• The change point when you connect the dots

• 9 workflow prompts you can run today

• Session timeline with timestamps

• Which workflow to run this week

🎁 Session resources: Installation and workflow materials in the BTL portal — slides, full recording with captions, and live outputs from all three demos.

How AI Use Has Advanced

Most people start using AI as a chatbox and gradually go through these three stages.

Stage 1 is the chatbox.
You ask a question, get an answer, go back and forth. This is where almost everyone started. It is also, for many people, where they still are.

Stage 2 is the advisor.
Claude becomes a thinking partner. You stress-test ideas, get second opinions on decisions. It is genuinely useful. But it stops short.

Stage 3 is the hand-off.
Claude does not advise, it executes. You stay in the conversation. Claude does the research. Claude edits visuals. Claude handles payments. You keep thinking. The mechanical work leaves the room.

MCPs (Model Context Protocol) are what make Stage 3 possible. They are connectors: a standard protocol that lets Claude communicate directly with the tools you already use. Gmail. Notion. Canva. Stripe. Vercel. If a tool exists, there is usually an MCP for it.

The technical setup is a few minutes. That is not the hard part.

Fix This Old Habit to Be Ready for Agentic AI

The reason most people do not use MCPs is that leaving the chatbox feels normal.

You have been doing it for years. A notification comes up, you click over.

You need to update a spreadsheet, you open a new tab.

You finish the Claude conversation, then go execute the thing it told you to do.

The break feels like work.

MCPs require you to notice that break and decide not to make it.

I use one rule to catch that habit. Whenever I am about to leave the chatbox to go do something, I stop and ask:

Does this have to be me?

If it is a one-time task, fine. But if I have done it twice, I look for an MCP. If one exists, I install it. If not, I build one.

The overhead of installing a connector is often less than ten minutes. The overhead of leaving the chatbox every time that task comes up is permanent.

If you are evaluating which MCPs are worth installing, I use these Claude MCPs daily and listed the installation guide and personal verdicts for each.

In the session, Eike mentioned a preference for CLI-first over MCP when a command-line interface is available: it connects more cleanly, machine-to-machine.

I agree for technical workflows. For most builders getting started, MCP is the lower-friction path in.

MCP Work Has 3 Types

Once you decide to stay in the chat, you need to know what to ask Claude to do.

MCPs can help in three ways. Most introductions only cover the first.

Retrieve is pure data gathering. Search LinkedIn. Pull a Reddit thread. Download analytics. This is mechanical work with no real risk — you are collecting information. Safe to start here.

Change is modification. Draft an email. Generate a Canva design. Create a Notion page. This is where people get cautious, because changes can be wrong. A sent email cannot be unsent. That caution is reasonable. Build in a review step before executing.

Execute is the full workflow. Claude builds the thing and ships it. A working product page with a live payment link. A deployed website. A complete process that runs while you are in another conversation. This is Stage 3 in practice.

The session had one live walkthrough for each level.

Read more

For the longest time, I collected. Strategies. Creator playbooks. Income models that looked like they could work. Every time I found someone building an audience, selling a course, or licensing an AI system, I saved what they were doing. Felt like I was getting closer.

Most people trying to make money with AI are doing the same thing.

AI accelerated the collecting. Retrieval, synthesis, summarization: what used to take hours now takes minutes. The research session of this program is entirely about running these tasks with AI.

But here is what nobody warned me about: the collecting habit survived. And collecting, by itself, does not earn.

Worse: once we had the data, we looked at the output, we read it, we nodded, we closed the tab. No decision made. No offer improved. No product launched. Then nothing happened.

The next time a similar question came up, we went the same route again. Same work, repeated from scratch, every time.

This article is about what comes after that. Not the collecting. The converting. Taking what you learn about how people make money with AI and turning it into something that keeps working: a decision, a digital mentor, an autonomous system that monitors what is working in your space while you build.

What’s inside:

• What the real bottleneck is after you get research results back

• 3 routes for turning research results into action

• 6 ways people make money with AI (and yours is no different)

• The full live build:

  • How to retrieve the best research output

  • How to hire your digital mentor (I hired Alex Hormozi)

  • How to build your tracking skill

  • How to build your autonomous system

• The complete loop connecting all 4 tools

• Live updates from the session: what people shipped, and questions from the group

• What to do after reading this

• Session timeline with timestamps

🎁 The full video, slides, and live-build outputs from this session are included in the Build to Launch member resources.

The collecting is the easy part now. What follows is the converting.

What the real bottleneck is after you get research results back

Before the live build, I want to name the exact moment this session is about.

You ran a search. You asked Claude something. You got results. And then you stopped.

Not because the results were bad. Because there was no obvious next step. The output was interesting but not actionable. It gave you more to think about, not less. So the research sat there, and eventually you moved on to something else.

This is not a motivation problem. It is a routing problem. Research results need a destination. Without one, they stay information. With one, they become decisions, systems, or leverage.

There are three places research results can go.

3 routes for turning research results into action

Read more

But the friction shows up fast.

How do you turn a big desire into a clear question?
How do you get out of tab-stuffing mode and actually move the needle?
Which tool should retrieve, which one should synthesize, and what kind of data is worth collecting in the first place?
How do you know when you have an answer that’s worth acting on instead of just another plausible paragraph?

Those questions are not new. They existed long before AI. What’s changed is that AI makes them much easier to answer if you know how to route the work.

At the start of this session, I ran a quick poll in chat and asked what people currently use for research. The answers came back as combinations: Claude and Perplexity. ChatGPT and Google. Forums and communities. Almost everyone was already using multiple tools.

That was the point. The tools are already here. The harder part is building a process that turns vague research into clear, actionable answers.

This article walks through that process: how to identify the question, how to route it, and how to decide what to do with the answer once it comes back.

What’s inside:

• The 3 research situations everyone hits:

  • How to learn something from scratch

  • How to track down a specific answer

  • How to pressure-test an idea before you commit

• The 5-stage process for turning a vague question into something you can actually route, retrieve, and act on

• 3 live demos run in parallel:

  • “How do I make money with AI?” -> what the crowded market still reveals

  • “Where can I get the best deal on a second-hand car?” -> how a specific lookup returns a completely different kind of answer

  • “Is it a good idea to build a deal tracker?” -> how to get directional signal before you spend the next week building

• How the MCPs fit into the process:

  • Perplexity for retrieval

  • Build to Launch MCP for the repeatable research workflow members can use

• Live questions from the session:

  • How the MCPs work together

  • How accurate AI answers are

  • Where to store research

  • A scheduled-task example for handling data over time

• What to do this week:

  • One question to run through the framework yourself (with 30 ideas to get you started)

• Program context:

  • Jenny’s background

  • CPD certification details

• Session timeline with timestamps

🎁 Everything from this session is included in the Build to Launch resources: the slides, the full video with captions, the research brief and outputs from all 3 live questions, and a list of question ideas you can use to run the same process on your own work.

The 3 Types of Research Questions

Before the live demo, I named the three situations most research questions fall into. This matters because the shape of the question changes what a useful answer looks like.

Type 1: Learning something new. You do not know enough yet to ask a sharp question. Example: you want to understand SEO, AI monetization, or how agents work before going deeper. The goal here is foundation.

Type 2: Data collection. You know what you need and now you need specific, current information. Prices. Listings. Rates. Providers. Tools. The goal here is not broad understanding. It is finding the right data fast.

Type 3: Idea validation. You have a thesis and want enough signal to decide whether to keep going. You are not looking for perfect proof. You are looking for enough evidence to invest the next week or redirect.

These question types can blur together. A question that feels like learning may actually be a decision question, and something that sounds complex may just need a straightforward lookup. Once you name the question type, the rest gets easier.

The 5-Stage Research Process

The framework itself is simple. It is not tied to one tool. It is just a way to stop treating every research question the same.

If you want to see how this process shows up in different kinds of work, here are three examples:

• Researching visual product covers without a design background

• Researching what was actually true about Substack Notes performance

• Researching whether an app idea was worth building before writing code

Stage 1: Refine. Turn the vague desire into a real question.

Stage 2: Lens. Decide whose perspective you are borrowing.

Stage 3: Retrieve. Go to the source that actually holds the data.

Stage 4: Synthesize. Force the results into a structure you can compare.

Stage 5: Decide. End with one next move, not five ideas.

That was the framework. Next, I ran all three question types live so you can see how the same process produces completely different outputs depending on the question.

In the live demo, I also use reusable research skills like idea-research and data-curator inside the Build to Launch MCP so I am not rebuilding the same research process from scratch each time.

The Live Demo

Read more

AI has so much to offer, and it’s very easy to get lost. New tools launch every week. Every announcement creates a little more urgency, a little more noise, a little more catching up to do. In the meantime, the actual problem you’re sitting with is still there.

This is where practical AI building becomes useful: you stop chasing what’s new and start solving what’s real.

How the program works

Each sprint runs for seven days. One live session. One problem you’ll actually build through.

Each 1-hr live session follows the same structure:

• 15 min — Context and tools. I introduce the problem, the first principles underneath it, and the tools we’ll use.

• 40 min — Live build. I build it in front of you, real setup, real workflow, real decisions as they happen. You see the thinking unfold, then take it wherever makes sense for you.

• 5 min — Discussion and Q&A. Open discussion on what we just covered, questions that came up, and how to take it further in your own work.

Before the live session: When you join, you receive a diagnostic. Fill it out and submit it. You receive a personalized assessment within 2 days after submission. It’s how I understand where you are and what you’re working on.

During the session (1 hour): Live build. You see the thinking unfold in real time, not a polished after-the-fact demo.

After the session (7 days): You stay in the inner circle. Ask questions, share what you’re building, get feedback from me and the group while the work is still active. After seven days, you leave with something built.

Past sprints

Sprint 1: Research
Get clarity on what opportunity to pursue in the AI space, and how to research it without drowning in tabs. You leave with a 5-stage research framework applied to one real question from your work, ending with a structured output you can act on.

You build a research brief using 3+ AI tools.

Sprint 2: Make Money With AI
Stop collecting income strategies. Build a system that turns them into decisions. You leave with a digital mentor built from a top creator’s public content: their frameworks and decision logic loaded into NotebookLM, plus a tracking skill and scheduled task that keep it current automatically.

You build a pipeline that flows

Sprint 3: Build Your AI Workflow Stack

Stop stacking tools you never wire together. You install one MCP live, from zero to working, then walk out with three real workflow demos built from real data and the templates to rebuild them yourself.

Sprint 4: Grow Your Substack

Hand your Substack growth to Claude. Eight workflows set up live in your own Claude, from digesting your inbox and sizing a niche to studying top creators, drafting and scheduling notes in your voice, and scoring what works, so the growth engine runs itself.

Paid subscribers keep the inner circle access between sprints.

The next sprint

Every sprint follows the same shape, so you always know what you’re walking into.

You show up already knowing your direction. Fill in a diagnostic at registration. Within two days, you receive three project options built around your specific situation: the project, the MCP to install, the first prompt to run.

The live hour covers where to find the right tools, installs one from scratch on screen, and demos three real workflows built from real data.

You leave with the recording, three workflow templates, and a Q&A portal open for seven days.

Subscribe or reply to any post to get the next date and topic first.

What you get

• Jenny’s personal feedback on your assessment. Before the live session, you submit where you are and what you’re working on. You get a direct response, not a template.

• Clarity on the tools and method. You leave the session knowing what to use, in what order, and why. No more “I watched it but I’m not sure where to start.”

• 7-day async window. After the live hour, you stay in the community for a week. Ask questions, share what you’re building, get feedback from me and the group while the work is still live.

Pricing

Per sprint (open to everyone)
$97. One sprint, one time. Assessment, live session, and 7-day community window. No subscription required.

Paid Build to Launch subscribers
$47 per sprint. Plus you keep community access between sprints, and annual subscribers get vault access (300+ prompts, 65+ checklists, 10+ guides, workflows).

Upgrade to Premium

Common questions

What is the assessment?
When you join a sprint, you receive a short set of questions about where you are and what you’re working on. Submit your answers at least two days before the live session. I read every response and send you personal feedback. It shapes how the session lands for you specifically, not just the group.

What’s the time commitment for a sprint?
One hour live, plus however much time you want in the 7-day window. Some people spend two hours total, some spend ten. You control it.

I’m not technical. Is this for me?
No coding required. No technical background needed.

The tools we use have no-code interfaces, you write instructions, not code. The skill this program builds is knowing what to ask for and how to evaluate what you get back. That doesn’t require a technical background.

I happen to be technical, but 80% of what I’ve built had nothing to do with code. I didn’t know how to launch a Gumroad product. I didn’t know how to publish on different platforms. I didn’t know how to put together free resources people would actually want. I didn’t know how to sell, how to write consistently, or how to create images. Most of what I’ve built lives in areas where I had no idea what I was doing, just curiosity and the need to move forward.

The hesitation is the starting point. AI gets you past it. That ability compounds over the next year, five years, ten years.

Do I have to attend live?
No. Live sessions are great for real-time feedback and community, but recordings are available if you can’t make it. All materials are shared async, so you won’t miss anything.

What tools do I need?
Whatever you already use is a fine starting point. This program isn’t tied to any specific tool, we work with whatever makes sense for what you’re trying to build. Most AI tools have free tiers, so you can get started without spending anything. I’ll share what I’d reach for each sprint and why, but you adapt from there. You don’t need to buy anything before Day 1.

Can my company pay for this?
If you want to expense this sprint or your Build to Launch subscription, I have email templates you can use to request reimbursement from your employer. Reply and I’ll send them over. I’m also a CPD-verified trainer (verification: 789656, verify at thecpdregister.com), which may help with your reimbursement request.

Is this program CPD-accredited?
Not yet. I’m a CPD-verified trainer, but the program itself is not currently CPD-accredited. If enough participants need that, I’ll pursue it.

Will I get a certificate?
Yes. You’ll get a Certificate of Completion from Build to Launch when you finish the program and complete your capstone. It could be an app, automation, workflow, digital product, or whatever you shipped.

Questions not answered here? Reply to any post or message me directly.

Why now

The builders who develop this reflex early will have a significant advantage over the next few years. They are those who stop chasing tools and start with practical AI building. Not because they know more, but because they can move faster, with less friction, on problems that actually matter to them.

You already use AI. This is the year you make changes with it.

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Build to Launch is more than a year old now.

When I started it, I thought this newsletter was about apps, building them with AI, solving the technical blockers, shipping to real users.

The core belief: anyone can thrive with AI.

That was the first layer.

Over 12 months of building, launching, failing, and resetting, I picked up skills I never expected: SEO, visual design, content systems, product packaging, writing. None of those were my domain. But AI got me there faster. Not because I became better at using AI. Because I got better at spotting friction in my own work and being willing to build something to remove it.

What I didn’t notice until recently: “building” had quietly changed meaning for me.

It started as apps.

Then it expanded to scripts, automations, workflows.

Then to anything that removed friction, in whatever form that took.

The term “building” had outgrown its original definition.

That shift is what practical AI building is about.

What practical AI building actually means

It is not about apps.

It’s not about websites, plugins, Chrome extensions, MCPs, or any specific format. Vibe coding is a method, a useful one, but not the point.

I’ve shipped vibe-coded products and interviewed a lot of builders who have too. And after all of it, I keep coming back to one thing Steve Jobs said in 1997:

“You’ve got to start with the customer experience and work backwards to the technology.”

Not the other way around. Not technique first, user second. Often, you are the user.

The builders whose stories stuck with me weren’t the ones with the most impressive tech. They were the ones solving something real, no matter how small.

If a script saves you an hour a week, that script is the product.

If an n8n workflow removes the copy-paste you’ve been doing for months, that workflow is the build.

What you create shouldn’t be limited by what sits on the surface. It should be determined by the problem you’re trying to solve.

I’ve started calling this vibe building.

Before AI, it was nearly impossible to build an immediate solution to your own problem. You needed technical skill, time, or someone to hire. Now you can describe the friction and let AI figure out how to remove it.

I have 12 automated jobs running right now on a server I set up with OpenClaw: daily briefings, content research, Substack monitoring, performance tracking. None of it because I’m a systems engineer. All of it because each one freed up time I’d rather spend building.

You encounter friction. Your first instinct is “can AI handle part of this?” And then you know how to make it happen. That reflex is what practical AI building is.

What the shift actually looks like

It doesn’t happen all at once. It’s a series of moves.

From following tutorials to recognizing patterns. Behind every tutorial are three layers: simple steps anyone can copy, technical challenges that need context, and domain expertise only you bring. Once you see those layers, you stop needing tutorials. You look at someone else’s build and pull out the pattern yourself.

From tool-hopping to tool-knowing. You stop asking “which AI tool is best?” and start asking “what am I trying to do right now?” The tool follows the task. You go deep on two or three instead of shallow on twenty.

From prompting to managing. You stop thinking about individual prompts and start thinking about systems. Context files. Constraint rules. Memory across sessions. You direct AI the way you’d direct a capable but literal colleague.

From one-off builds to compounding systems. Your first AI build is a one-time win. Your tenth connects to the others. The research feeds the content. The content feeds the distribution. The distribution feeds the iteration.

None of these shifts come from watching. They come from repeated contact with your own real problems.

The real shift doesn’t come from a methodology article about prompting frameworks, I’ve written a lot of those, so I can say this honestly. It comes the first time you use one prompt to create something you needed, and it works. Not something impressive. Something that removed the thing slowing you down. That one step. That joyfulness is what keeps you going.

Why most people don’t get there

Two patterns come up constantly.

The tool spiral:

New AI tools launch every week. People try each one, hit the free tier, save the bookmark. Six months later they have 40 bookmarks and no system. The problem isn’t falling behind on tools. The problem is never going deep enough with any one of them to get something real out of it.

The one-bad-answer wall:

Someone asks AI a question, gets a hallucinated answer, concludes AI doesn’t work for their field, and stops. One bad output kills months of potential. But AI giving a bad answer is normal. The skill is catching it, redirecting it, and continuing. That part, nobody teaches.

Both patterns come from the same place: learning about AI tools without developing AI thinking.

I know this one personally. It took me several months to go from skeptical to genuinely optimistic about what AI could do for my work. My family went through the same arc. Scientists I know. Colleagues I’ve worked with.

The shift happens. But it usually needs a push, some structure, and someone alongside you when it gets frustrating.

What stays constant when the tools change

Every week there’s something new: a better model, a new agent framework, a tool supposedly replacing the last one. This is real and overwhelming. I feel it too.

But two things don’t change with it.

1. The instinct.

The people building fastest aren’t using the newest tools. They’re deep on a few. They’ve spent enough time with them to know what they’re good for, and they apply that understanding quickly when something better comes along. The tools change. The instinct for how to apply them to your own problems doesn’t.

2. Your domain expertise.

You don’t need to become technical. If you’re a writer, a researcher, a psychologist, a teacher, a specialist in anything, you already have the domain knowledge. You know the work. You know which parts slow you down — the tasks you repeat and resent, the things you do out of obligation rather than because that’s where your real value lives.

Those are exactly the places AI earns its place. Not to replace your expertise. To remove the friction around it. The domain you already have is the goldmine. AI is how you go further inside it.

Put those two together and something starts to compound. One build that works. Then another. Then a system that runs while you sleep.

None of that happens by accident.

A structure for making it happen

That’s why I built the Practical AI Builder Program: a 12-month cohort where we work through one real problem per month, together.

I started it because I kept seeing the same thing: people who know enough to want more, but don’t have a container to make it real.

Not a course to watch. Not a community to lurk in. One real problem per month, one method to tackle it, and a place to actually try, with other people doing the same thing.

If you’re already a paid subscriber, you’re in. The first session is April 10.

If you’re not yet, this is what the program is designed to do: give you the push, the structure, and the company to close the gap between “I use AI” and “I build with it.”

Practical AI Builder Program →

Practical AI Builder Program

Not sure yet? Take the one minute survey below to tell me where you are. I read every response and it helps me shape the program around the people actually in it.

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See you from the other side,

— Jenny

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