I used to think the same.

So I spent months collecting prompt tricks, magic words, and those giant “perfect prompt” templates people love to share.

Some of them helped.

But my results were still hit or miss, and I couldn’t figure out why.

Then it clicked. The problem wasn’t the prompts. The problem was that AI kept answering before it actually understood what I wanted.

Think about it.

If you ask a coworker to write a report and they start typing two seconds later, without a single question, you know it’ll miss the point.

A fast answer with no context is usually an average answer.

AI works the same way.

When you give it an empty brief, it fills the gaps with guesses. When AI guesses, you get generic output.

The good news is you can fix this without memorizing another prompt formula.

You only need to change one thing: make AI understand the task before it starts working on it.

Here’s what we’ll cover:

• The Briefing Loop, the exact system I use to get better first drafts

• Real examples for decision-making, writing, and more

• How to save this context so you don’t have to repeat yourself every time

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The Problem: AI Responds Too Fast

One of the biggest problems with AI is that it answers even when it has no clue what you actually want.

If you ask it to translate a sentence, fix a typo, or reformat a table, a fast answer is totally fine, but if you ask it to write an article, prepare a class, analyze a dataset, design a proposal, or help you make a decision, a fast answer is usually a bad sign.

Here quality doesn’t come from the model.

It comes from context.

And when the context is missing, AI just fills the gaps with average answers. That’s why so many outputs sound the same. Not because everyone uses the same tool, but because everyone feeds it the same empty instruction.

So what we want is simple. AI should start working only after it understands the task, not before. This is what this guide is about.

Prompting Is Asking. Briefing Is Preparing

A prompt says:

Do this.

A brief says something like:

- This is the goal.
- This is who it's for.
- This is the tone.
- These are the limits.
- This is what I want to avoid.
- This is what a good result looks like to me.

Most people try to get better results by writing better prompts.

That helps, but only up to a point.

The real jump comes from somewhere else. It’s about giving direction and purpose to what you’re building, not just cleaner-sounding instructions.

So the clearer the brief, the less the model has to guess.

AI’s First Job Shouldn’t Always Be to Answer

This is the most important shift.

That shift changes the whole conversation.

AI stops acting like a machine that spits out text and starts working more like an editor, a consultant, a teacher, or a sparring partner.

Sometimes the best first response from AI isn’t an answer.

It’s a question.

Or even better, a short list of questions that show you what you haven’t made clear yet.

Picture this. You tell it:

I want to write an article about productivity

A bad interaction is AI writing the article right away.

A better one is AI asking you first:

- Who is the article for?
- What problem do you want to solve?
- What common idea do you want to challenge?
- What examples do you want to include?
- What should the reader be able to do after reading it?

That already makes the article better before you write a single line.

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The Solution: The Briefing Loop

Here’s the system.

It’s not complicated. But it completely changed the way I used to work with AI.

The goal is to stop AI from answering fast but badly.

Step 1: Start With a Rough Task

You don’t need to start with a perfect prompt.

That’s actually the whole point. You can start with something simple:

I want to write an article about why people get poor results from AI

This isn't a brief yet. It's just the rough task. It tells AI what you're trying to do, but it doesn't give it enough context to do it well.

That's exactly why the next step matters.

Step 2: Ask AI to Build the Brief Before Answering

Don’t ask AI to do the task yet. Ask it to turn your rough task into a proper brief first.

Use something like this:

Before you answer, turn my rough request into a proper brief.
Identify what context is missing or ambiguous.
Ask the fewest questions possible.
Only ask questions whose answers would change the final output.
If a question is nice to know but not necessary, skip it.
Ask no more than 3 questions for small tasks and no more than 5 questions for complex tasks.
Don't generate the final output yet.

This forces AI to pause.

And instead of rushing to finish the task with half the information, it helps you clarify the work first.

So before AI responds, you check:

This checklist helps you reduce the guessing and, as a result, get better outputs.

After putting together the prompts from steps 1 and 2 and running them, expect some back-and-forth before Claude fully understands what you want. Once you’re both aligned, move on to step 3.

Step 3: Have It Restate the Brief

This step looks small, but it saves you from a lot of mistakes.

Before generating the final output, ask:

Before writing, restate the brief in your own words.

Include:

- Goal
- Audience
- Angle
- Constraints
- Expected format
- What to avoid

Why does this work?

Because if AI got the brief wrong, it will also get the result wrong. And it’s way easier to fix a five-line restatement than to fix a whole draft.

Step 4: Only Then Generate the First Version

Now it’s ready.

AI knows what it has to do.

It knows who it’s for.
It knows what to avoid.
It knows what format to follow.
It knows what “good” looks like.

That doesn’t guarantee a perfect answer. But it makes the first version a lot stronger.

And a strong first version matters more than it seems. Because when the first version is weak, everything after it turns into patches. This way, you save all the time you'd otherwise spend fixing the mess a bad brief creates.

Optional: Run a Gap Review

Once you get the first result, don't ask:

Can you improve it?

That’s too limited.

Ask instead:

Review this result against the brief.

Tell me:

1. What’s strong
2. What’s still vague
3. What’s missing
4. What should be removed
5. What would make it more useful for the audience

This way AI doesn't just produce. It also becomes an evaluator.

Putting It Into Practice

Example 1: Writing an Article

Weak approach:

Write me an article about using AI better.

Better approach:

I want to write an article for professionals who use AI every day but still get generic answers. The core idea is that the problem isn't bad prompts, it's bad briefs. 
Before writing, review this brief. Ask me only the 5 most important questions needed to make the article specific, useful, and not generic.

AI might ask:

• Who exactly is the reader?

• What kinds of tasks do they use AI for?

• What belief do you want to challenge?

• What examples do you want to include?

• What should the reader do differently after reading?

That already makes the article better before you write a single line.

Example 2: Teaching a Complex Topic

Weak approach:

Explain confidence intervals to me

Better approach:

I need to explain confidence intervals to students who are learning statistical inference for the first time. Before explaining, ask me what you need to know about their current level, the notation they know, the examples they've already seen, the mistakes they usually make, and whether the explanation should be intuitive, formal, or both. Don't explain anything yet.

This completely changes the answer. Because "explain confidence intervals to me" can mean a lot of different things: a simple analogy, a formal derivation, an exam solution, or a script for a 10-minute class.

Example 3: Data Analysis

Weak approach:

Analyze this dataset

Better approach:

I'm going to analyze a dataset for a business report. 
Before suggesting any analysis, ask me the essential questions about the business objective, the report's audience, the available variables, the decision the analysis should support, the expected technical level, and the final deliverable format. Don't start the analysis until the brief is clear

Because with AI you have to accept one thing: "analyze the dataset" isn't a goal. Analyze it for what? To predict, compare groups, find patterns, detect anomalies, make a decision, build a dashboard, or train a model? Each one points to a different analysis.

Example 4: Making a Decision

Weak approach:

Should I do this?

Better approach:

I'm evaluating this decision: [describe the decision]. 
Before recommending anything, ask me the minimum number of questions needed to understand my goal, my constraints, the alternatives, the risk of being wrong, the cost of waiting, and what information would change the decision. Then give me a recommendation with trade-offs

Here, the method stops being just about productivity. It turns into a way of thinking and getting clarity, because the right questions:

• Force you to clarify what you actually want.

• Reveal constraints you never mentioned.

• Surface risks you’ve been ignoring.

• Help you separate a preference from a real decision.

One More Thing

You don’t need to use the Briefing Loop for everything.

Use it when the task involves ambiguity, judgment, or real consequences.

The key question is this:

Does AI need context to do this well?

If the answer is no, just ask directly.

And one last thing.

If you keep explaining who you are, how you write, who your audience is, what tone you use, or what kind of results you expect, you’re basically writing the same brief over and over by hand.

That’s where context files come in.

A context file is a permanent brief. You write it once, and AI reuses it every time.

I just released My AI System. It contains a skill that generates context files tailored to your needs. Check it out: artificialcorner.com/p/ai-system

If this helped, subscribe to Artificial Corner. I share practical AI guides, Claude workflows, and systems you can actually use the same day you read them.

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If you’re new here, welcome! I’m Frank. My online writing journey started in 2021, when I began publishing on Medium(dot)com. I grew to 150,000 followers there and then moved to Substack in 2023 where I share weekly AI guides.

You’re reading this because you like the way I write.

The Claude guides, the emails, the posts.

They’re easy to follow. Sounds like a friend teaching you what he’s learned, not a robot.

But here’s what you might not know.

Claude writes the first draft. Not from a generic prompt. Not from “write in my style.” I built a system around my voice.

For every project, Claude has access to files that shape the writing. These files teach Claude how I write: the words I like/dislike, the analogies I like, the punctuation I hate, the structure my guides follow, and more.

After Claude generates the first draft, I do the final pass: I add personal knowledge, make the visuals, delete what I disagree with, sharpen the examples, etc.

I make it feel human.

So the system doesn’t replace me.

It gives me a strong first draft in my voice (fast) and lets me spend my energy on the parts that actually matter. That’s the Claude system behind the writing 70,000+ of you read here. And the system I’ll share with you today.

Not as a “copy my newsletter” template (most of you don’t run a newsletter).

The backbone.

The structure you can adapt to your own projects and workflow.

All in your voice.

But before I hand it over, let me show you where it lives and what the folder contains.

Why this system lives in Claude Cowork

Over the past three years, I’ve built and run my business with AI at the center. Along the way, I’ve relied on three main tools: ChatGPT, Claude Cowork, and Claude Code.

Each one taught me something.

ChatGPT is the easiest to start with, but you’re stuck in a chat bubble. You ask, you copy, you paste, you fix. It never touches your real files or remembers how you work.

Claude Code is the most powerful AI I’ve ever used. There’s just one catch: the more power you want to unlock, the more technical you have to get. For most of you, that’s a wall.

Claude Cowork sits right in the sweet spot.

• Cowork is as easy to use as ChatGPT

• Cowork has a big chunk of the power of Claude Code

• No coding. No terminal. No complex setup

A real system used to mean going technical.

Cowork killed that trade-off: you can ask Cowork to generate drafts, customize the system, and develop it using plain English instructions (the same way you’d ask a coworker for a hand).

So that's where I built this. You get the system, not just prompts.

What’s in the AI system folder

Here’s what the AI system folder contains:

• Context files: they teach Claude your writing voice and who you write to

• Claude skills: the core skill that generates your context files, plus two additional skills that turn videos and articles into usable source material for Claude

This is the foundation of the system.

For this first version, I kept only the essentials: the pieces anyone can adapt to their own projects (the context files). I’ll keep adding new Claude skills, new files, and new upgrades that plug into the same system in the coming months.

That said, this first folder has everything you need to make Claude write in your voice for whatever project you have in mind.

Download my Cowork writing system

This is the core system behind everything I publish.

The only reason I can keep building it (and keep the free guides free) is the support of paid subscribers. Become one today and it's yours!

All paid subscribers get:

• The full kit: the context profiles and Claude skills

• Monthly updates: New Claude skills, project files, and more to keep the system growing

In addition to that, annual subscribers get (only this week):

• voice-dna-frank.md: In case you want to skip the setup and write your drafts with my voice

By the way, this month Artificial Corner turns 3 years old.

The first 20 people who go annual get a 20% discount

Click here for the discount: https://artificialcorner.com/endsunday

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🔒 Paid Substack members: link to get the files below

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For a long time, I used AI the same way many people do.

I’d ask a question, copy the answer, and move on.

It felt productive. I was saving time, getting things done faster, and checking more boxes. But at some point, I realized something uncomfortable: I was getting answers faster, but I wasn’t thinking any better.

AI was doing the work, and I was just collecting the output.

Now, instead of treating AI like a vending machine for answers, I use it to improve my inputs, learn faster, challenge my decisions, and free up time for the work that actually needs me.

The difference was bigger than I expected.

I wasn’t just faster. I was sharper.

In this guide, I’ll show you how to make that shift.

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1) The real shift: using AI as a thinking partner

Most people use AI like a calculator. You type a question, an answer pops out, problem solved.

But AI doesn’t work like a calculator.

A calculator gives you precise, consistent results every time. AI can be brilliant, fast, and creative… but it can also sound super convincing while being completely wrong (and that’s the tricky part).

So the worst way to use AI is to treat it like a vending machine for answers. You put a coin in, you grab whatever falls out.

The better way? Treat it as a thinking partner.

Don’t just measure how much time AI saved you. Measure how much better you were able to think because of it.

Here’s what that shift looks like:

2) Better inputs, better ideas

AI can speed up your results. No doubt.

But if your inputs are poor, your outputs will be poor too. They’ll just arrive faster.

Before you obsess over prompts, take a look at what you feed your brain every day. If your attention is scattered across shallow info, random content, and updates that add nothing to your work or learning, AI won’t fix that for you. At best, it’ll help you summarize the mess a little faster.

The smart move is the opposite. Use AI to improve the quality of what enters your mind, instead of drowning yourself in trivial stuff.

Here are three strategies you can start using today:

a) Use AI to curate information, not to flood you with more

One of the most useful things you can do is build a daily briefing around the topics that actually matter in your field.

Instead of opening ten tabs, half-reading headlines, and slipping into autopilot, you can ask AI to do the first scan, pull out the essentials, and hand you only what matters.

Here’s a prompt I use a lot:

Act as my research analyst. Find the three most important developments about [topic] in the last 24 hours. Summarize each one in two sentences, explain why it matters, and tell me what strategic question I should be asking myself because of it.

And you get something like this:

I know at first glance this looks like just another prompt. But what I’m really doing is cutting the noise without cutting the thinking. I want AI to give me context, not to think for me.

b) Don’t learn “just in case.” Learn “just in time”

Another common mistake is hoarding content you never actually use.

It looks like this:

Learning this way feels responsible, but most of the time it’s just an illusion of progress. You read a lot, apply little, and remember even less (we’ve all been there).

So flip it. Learn the thing right before you need to use it.

A couple of examples:

• You have a pricing meeting tomorrow → learn what you need about pricing a few days before

• You’re designing an executive presentation next week → study how to structure executive arguments now.

When learning is tied to an action that’s right in front of you, it sticks way better. AI just shortens the distance between “I read it” and “I used it.

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3) Use AI as a tutor

When AI hands you the answer too fast, it feels efficient. But feeling efficient and actually learning are two different things.

Learning isn’t just getting exposed to information.

Learning means putting in effort, recalling stuff, messing up, adjusting, and trying again.

So if you want to use AI to get smarter, you have to change the job you give it.

Start using AI as a tutor. Here’s how to think about it:

a) The problem with getting the solution too fast

Really understanding something means you can explain it in your own words, use the idea in a different situation, answer new questions about it, and catch the holes in your own reasoning.

For that to happen, AI can’t do all the work. It has to make you show up too.

b) The best setup: you try first, then AI corrects you

Here's a really powerful way to use it:

For example, one prompt I use:

I just studied this topic: [topic]. Ask me questions one by one. Don't give me the answer until I try to respond. After each answer, tell me what I got right, what I missed, and then ask me a harder question.

This turns AI into a tutor. It walks you through the whole process of locking in what you just learned (and it's surprisingly good at it).

c) Turn friction into part of the learning process

There’s a simple idea here. For information tasks, reduce friction. For learning tasks, keep some of it.

In plain terms:

• If you want to catch up fast → let AI be fast.

• If you want to truly learn → let it make you work for it.

So under that logic, you can ask AI:

The real advantage of AI here isn’t that it “knows a lot.”

The advantage is that it’s always there, it never gets tired of explaining the same thing, and it can adjust to your level on the spot (try getting that from a textbook).

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4) Make AI attack your ideas before reality does

Almost every bad decision has the same thing behind it: someone too close to their own idea.

When you’re the one who came up with it, you see the potential, the excitement, the promise. What you don’t see as easily are the cracks.

That’s where AI gets really valuable. Not just to help you think an idea through, but to rip it apart on purpose.

a) The most common mistake: using AI to get a pat on the back

Most people drop a plan into AI and ask, “What do you think?”

The problem is that if you don’t steer it, AI tends to play nice. It’ll summarize, validate, and tidy things up, but it won’t push back with the rigor you actually need.

And if all it does is hand you a prettier version of what you already wanted to believe, it’s not helping you think. It’s just agreeing with better grammar.

So ask for opposition, not approval.

Here's the skill I use:

## Sycophancy Skill

You are my critical thinking partner. Your default mode is constructive disagreement.

1. Before agreeing with anything I say, identify at least one assumption underneath it that I have not tested. State the assumption plainly.
2. When I propose a decision, idea, plan, or interpretation, your first response is to argue the strongest opposing case. Do not soften it. Do not append “but you might be right.” Make me defend my position.
3. If I push back on your counterargument, do not retreat because I objected. Retreat only if I produce new evidence, new reasoning, or a constraint I had not mentioned. Saying “fair point” without new information is not enough.
4. When I share work to review, identify what is weakest first, not what is strongest.
5. If I am clearly emotionally invested in an answer, name that explicitly and ask whether the emotion is signal or noise.
6. If you cannot find a real flaw, say so directly: “I have looked for the weakness and I cannot find one.” Do not invent a flaw to perform thoroughness.
7. End every substantive exchange with one question I should sit with before I act, not a summary.

Tone: direct, not aggressive. Specific, not abstract. One disagreement at a time. Cite my own words when challenging me. Do not open with praise, do not use “great question” or any flattery, do not hedge with “I could be wrong but,” and do not add a closing reassurance.

You can also use prompts to turn AI into a sparring partner.

Here are 3 good prompts:

Prompt #1:
Imagine this plan fails six months from now. Walk backward and tell me the most likely reasons why it failed.

Prompt #2:
Act as a smart, cynical competitor. Analyze this plan and tell me how you'd exploit its weaknesses.

Prompt #3:
Rank the three main risks of this plan by probability and impact. Then give me one preventive action for each.

b) The real value: protecting you from your own blind spots

What makes this so useful is that it doesn’t even need AI to be perfectly right. It forces you to look where you normally wouldn’t, and that alone sharpens your judgment.

So AI becomes a kind of honest mirror, as long as you tell it to stop nodding and start arguing.

Before any big decision, you can run this map:

5. A simple system to use AI better every week

Getting smarter with AI doesn’t come from finding the perfect prompt.

It comes from running a few thinking loops on repeat.

You can start with something simple:

a) Every day: improve your inputs

Use AI to get a short briefing on your field. The goal isn't more information. It's better information.

Prompt:

Give me the three most important things I should understand today about [topic]. For each one, tell me what happened, why it matters, and what question I should ask myself.

b) Every time you learn something: turn it into practice

Don't stop at the explanation. Push it to quiz you.

Prompt:

Ask me questions about this [topic] one at a time. Don't give me the answer until I've tried. Then correct my reasoning and crank up the difficulty.

c) Before an important decision: ask for opposition

Skip the validation. What you want here is resistance.

Prompt:

Before you agree with me, point out the main weakness of this idea and the most dangerous assumption I'm making.

d) Before creating something: improve the brief

Resist the urge to ask for the final output right away. Clarify the problem first.

Prompt:

Before writing, tell me what you think I'm trying to achieve, who the audience is, what tone makes sense, and what info is missing to do it right.

Then, at the end of each week, look back at what you delegated and what you protected. A few questions worth asking yourself:

• What repetitive tasks could I have handed off?

• Where did I use AI to think less?

• Where did I use AI to think better?

• What decision got better because I questioned it first?

• What part of my work really needed my own judgment?

One more thing

Now that you’ve seen the whole framework, don’t just nod and close the tab.

Build your own weekly system for thinking better with AI:

I wrote this guide to help you use AI to get smarter.

Becoming a paid subscriber gets you my full library of guides, the prompts and systems I actually use, and new ones every week.

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Let’s connect! You can find me on LinkedIn, Instagram and X

You can have the best prompts in the world, but if you’re fighting your computer every time you switch windows, snap a layout, paste a screenshot, attach a file, or keep your screen from dimming on a long Cowork run, Claude is going to feel harder to use.

The apps below remove the friction around Claude.

In this guide, you’ll learn:

• The 8 Mac apps that pair best with Claude (+ Windows alternatives)

• The exact use case that earns each one a spot on your dock

• How I make the most out of these apps

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I used to blame my prompts when Claude gave me mediocre results.

Turns out, the problem was sitting in my folder the whole time.

Files named final.docx, final_v2.docx, new_final_REAL.docx. Old notes mixed with current drafts. No clear structure, no clear order.

Claude was doing its best with a messy workspace. And messy workspace = messy output.

A clean file system changes that. Claude knows what to read, what to use, and where to save the result. The output gets better without changing the prompt at all.

In this guide, I’ll show you the system I built: five folders, simple naming rules, and a structure Claude (and you) can actually navigate.

Here’s what we’ll cover:

• Why your folder is now part of the prompt (and what happens when it’s a mess)

• The five-folder structure that gives Claude a clear map of your workspace

• How to name files so Claude knows what’s inside before opening them

• The difference between active work, reusable context, and old files

• The full system, all in one view

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1. Your folder is now part of the prompt

A prompt isn’t just the text you type.

It’s also everything in your folder:

That’s why a messy folder gives Claude messy context.

It might open the wrong draft. Treat an old note as if it were current. Or ignore the file that actually matters. Instead of doing the work, it ends up spending time trying to figure out your workspace.

A well-organized folder removes that guesswork. It tells Claude where things are.

The goal is to make the folders easy to understand. For you and for Claude.

2. Build a workspace Claude can navigate

You don’t need a new system.

Your current Claude system just needs to handle more files without turning into noise.

The foundation stays simple:

YOUR-WORKSPACE/
├── system/
├── context/
├── projects/
└── outputs/

But when Claude starts working with many files, two small adjustments help:

1. Number the first level of the workspace

2. Add archive/

YOUR-WORKSPACE/
├── [01] system/
├── [02] context/
├── [03] projects/
├── [04] outputs/
└── [99] archive/

I use [99] archive/ so it always stays at the end. It’s not active work, but it stays searchable.

Same workspace. Easier to navigate.

[01] system/ → rules that Claude must follow
[02] context/ → stable context that Claude must know
[03] projects/ → active work
[04] outputs/ → completed work
[99] archive/ → old, but searchable

That’s enough. You don’t need twenty folders or a perfect taxonomy.

You need a structure Claude can scan quickly and that is:

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3. Name files so Claude (and you) can understand before opening them

File names are context.

Before Claude opens a document, the name already tells it what that file might be.

Avoid this:

• final.docx

• final_v2.docx

• new_final.docx

• final_REAL.docx

• notes.md

• screenshot.png

Use names that answer three questions: When? What? What status is it in?

Let’s look at an example:

Not every file needs the same level of time precision. Use the level that matches the file type:

A few quick rules:

1. YEAR → best for long-term files

2. YEAR + QUARTER → best for planning cycles

3. YEAR + MONTH → best for recurring work

4. FULL DATE → best for task-specific files

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4. Separate active work, reusable knowledge, and old context

Not all files need the same treatment.

Some are active.
Some are reusable.
Some are already finished.
Some are old, but might be useful later.

Don’t mix them.

• projects/ → active work

• context/ → stable context and reusable references

• outputs/ → finished work

• archive/ → old, but searchable

Numbering helps when you need a clear path. Claude knows where to start, which folder has the rules, where the active work is, and where the results go.

That doesn’t mean every file needs a number, though.

Inside [03] projects/, numbering makes sense because there’s a workflow:

[02] Projects/ 
└── claude-file-system-article/ 
├── 00_BRIEF.md 
├── 01_SOURCES/ 
├── 02_NOTES/ 
├── 03_DRAFTS/ 
├── 04_VISUALS/ 
└── 05_FINAL/

That order tells Claude:

[02] context/ works differently. Think of it as a library, not a workflow. There's no universal reading order here.

[02] context/ 
├── HOW-I-WORK.md 
├── voice-guide.md 
├── audience.md 
└── references/   
  ├── brand-guide.md 
  ├── article-examples/ 
  ├── prompt-library/ 
  └── source-library/

Reusable files don't need to be copied into every project.

Just tell Claude to use them when needed:

• Reusable across projects → context/

• Specific to one project → projects/

• Finished work → outputs/

• Old but useful later → archive/

Here’s the rule I follow for each folder:

Workspace → numbered map
Projects → numbered workflow
Context → descriptive library
Outputs → date + topic + status
Archive → old but searchable

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5. This is how everything looks together

The complete system doesn’t need to look complicated.

It just needs to show Claude where everything is.

YOUR-WORKSPACE/
├── [01] system/
│   └── SYSTEM-RULES.md
│
├── [02] context/
│   ├── HOW-I-WORK.md
│   ├── voice-guide.md
│   ├── audience.md
│   └── references/
│       ├── brand-guide.md
│       ├── article-examples/
│       └── source-library/
│
├── [03] projects/
│   └── claude-file-system-article/
│       ├── 00_BRIEF.md
│       ├── 01_SOURCES/
│       │   ├── 2026-06-24_video-notes.md
│       │   └── 2026-06-24_cowork-workspace-notes.md
│       ├── 02_NOTES/
│       ├── 03_DRAFTS/
│       │   └── 2026-06-24_claude-file-system_draft-v1.md
│       └── 04_VISUALS/
│           ├── workspace-map.png
│           └── file-name-structure.png
│
├── [04] outputs/
│   └── 2026-06-24_claude-file-system_final.md
│
└── [99] archive/
└── old-projects/

Don’t memorize the structure.

Memorize the rule:

• Markdown for instructions.

• Numbers for order.

• Names for meaning.

• Dates for search.

• Archive for noise.

That’s all.

Claude doesn’t need a perfect folder. Just one that tells it what to read, what to use, what to ignore, and where to save the result.

One more thing

Don’t reorganize your entire computer tonight.

That’s how people turn file management into another project they never finish.

Start with one folder Claude already touches.

Add structure.
Rename the files that matter.
Move the old stuff to [99] archive/.

That’s enough.

Give Claude fewer reasons to guess. That’s all this is.

If you want more practical guides like this, for Claude, Cowork, and AI workflows, the paid subscription is where you'll find them.

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This guide does the same thing on my phone.

I use Claude Computer Use + Mac's iPhone mirroring to control my iPhone with plain-English instructions, on a schedule, without me being there.

Sounds simple. It wasn’t.

Anthropic's safeguards pop up an "Allow for this session" request every single time Computer Use runs — meaning you have to be at your Mac to babysit it. That kills the whole point.

It took me over a week to crack it. There's no other guide for this anywhere (only Anthropic's basic Computer Use demos). I was so convinced this works that I bought a second Mac Mini just to run it (almost) 24/7.

I was tempted to title this guide "How to Make Claude Run Your iPhone 24/7." But I know most of you don't have a spare Mac and an old iPhone sitting around — and you don't need them. Not at the start.

With your current Mac and current iPhone, you can run this while you sleep. Nobody’s using your devices then anyway, so the same setup still works for you.

Here’s what’s behind the paywall:

• How to make Claude control any iPhone app

• The workaround that bypasses Anthropic’s “Allow for this session” popup (the one obstacle that kills every other Computer Use automation)

• The setup that lets Claude control your iPhone while you sleep (+my full 24/7 setup)

• My framework for picking which iPhone apps are worth automating (and which ones aren’t)

Before you follow this guide, check the requirements:

• A paid Claude subscription. Pro ($20/mo) is enough for the “while I sleep” version (a handful of overnight runs). If you want Claude running tasks throughout the day too, you’ll need the Max plan to cover the usage.

• Mac running macOS Sequoia 15+ (Apple Silicon or T2 chip), iPhone on iOS 18+. The setup relies on iPhone Mirroring, which doesn’t exist on Windows or Android (if you know a way to see and control your phone on Windows, this guide still works for you, though)

• You’ll need a free ChatGPT account. It’s part of the workaround that makes the automation run while you sleep. If you want to go 24/7, you’ll need a paid ChatGPT subscription

Honestly, this is one of the best guides I've written this year. I looked everywhere for a tutorial like this and came up empty — just Anthropic's basic Computer Use demos. So I spent over a week of trial and error building it myself.

You're getting that week, condensed.

If you’ve gotten value from my free posts, the paid tier is what keeps them coming. Hit the button below to unlock the full setup and join hundreds of readers who’ve already become paid subscribers.

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You asked AI to do serious work once.

Something you’d ask one of your best employees.

Sadly, the result was mediocre. Most people would think the tool failed them.

Here’s what actually happened: you compared a day-one intern to your best employee.

The fix isn’t a smarter model or a magic prompt. It’s a system that points AI at the parts that slow down your business.

I built an AI system for my business (mostly with Claude). Thanks to it, my small team handles work that would normally take twice as many people.

Below are the rules I follow to use AI in business:

• You don’t need to become “an AI business”

• Point AI at one real task (not your whole job)

• Set aside a few hours for a meeting with AI (make it do the middle 80%)

• Train AI like a new employee

• How to write the instructions for your AI employee

After weeks, I finally cracked how to make Claude control my iPhone (and do tasks for me 24/7)

The full step-by-step guide drops next week — exclusive for paid subscribers. If you’re getting value from Claude, consider becoming a paid subscriber today (you’ll get full access to my best guides)👇

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1. You don’t need to become “an AI business”

Nobody calls themselves an internet business anymore.

You just use the internet to sell, deliver, and talk to people, like everyone else.

AI is heading the exact same way. Your work doesn’t have to become an AI thing. You just need to use AI inside it.

The people quietly winning with AI right now aren’t running futuristic robot companies. They’re running boring, normal businesses (a local service, a small consultancy, a paid community) and using AI inside them to do more with a smaller team.

My team is a good example of this. Thanks to Claude, we can handle work that would've taken at least twice as many people.

But you shouldn’t only “use AI.” Many people now use AI.

This is becoming more like knowing how to use Word or Excel: useful, yes, but no longer impressive. The edge comes from how you use it and what you can actually produce with it.

This leads us to the next point.

2. Point AI at one real task (not your whole job)

I've been obsessed with AI since the ChatGPT release. So trust me — get one task done successfully with AI, and you'll want to hand everything off to it asap.

Don’t make that mistake!

Here’s a lesson I learned from Dan Martell.

First, ask yourself: Where is your work actually stuck?

Every workflow has a chain. For a business it looks something like:

Reach → Leads → Sales → Delivery → Retention

For your job, the chain might be:

Requests → Triage → Work → Delivery → Follow-up

Now ask yourself: if I 10x this task tomorrow, would my revenue actually go up?

The stage where the answer is a clear “yes” is your bottleneck. That’s where AI goes first. Not where AI looks coolest, where you’re stuck.

Here’s an example: I run a newsletter about AI (this one). There are dozens of tasks I could automate, but, believe it or not, generating articles with AI wasn’t at the top of my list. My bottleneck was reach, so I built automations to republish my free articles on other platforms and also adapt them into short posts for social media. Thanks to that, new readers found my newsletter and subscribed. Things have been great since then :)

Again, resist the urge to automate your entire job in one weekend. Start with one task.

If you don’t know where to start, write down what you actually do in a day and try this:

Here’s a list of my daily activities: [paste your list].

Which one of these can I use AI for, and which tool should I use? Walk me through it step by step.

For most tasks, Claude Cowork will be the solution.

We’ve built ready-made automations with Cowork: an invoice tracker that scans your inbox and flags who hasn’t paid, a prompt that turns any meeting transcript into action items (with owners and deadlines), a downloads-folder organizer.

Copy, paste, done.

Here’s what the invoice tracker gives you after one run:

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3. Set aside a few hours for a meeting with AI (make it do the middle 80%)

Whether you have a small or big team, remember this: You should be the first to automate your workflows with AI, not the last.

Why? Because you know the business more than anyone else. You might not be technical, but you can spot the bottleneck, define what success looks like, and what’s actually worth your team’s time

Once you’ve automated one of your own tasks, you understand how this stuff actually works, and explaining it to others becomes easy.

Also, you’re not being the boss who says “use AI” without ever having done it.

You’re leading by example.

I’ve done this myself. Every day, I set aside a few hours to spot what tasks can be automated with Claude. Once I know what to automate, I do this:

• I ask Claude Cowork whether it can handle that task (and how)

• I go back and forth with Claude until we’re on the same page

• Once I’m happy with Claude’s plan, I tell it to execute it

• I hit edge cases during execution. That’s fine. I don’t give up until the task is done

• Once AI succeeds, I turn the chat into a skill or a scheduled task

You don’t have to do this every day (as I do). Set a few hours per week.

Protect it like a meeting with your most important client, because that’s what it is.

One warning before you automate everything that moves: some work should stay yours. Two things you shouldn’t use AI for:

1. making critical decisions

2. building relationships

That said, you can still automate everything around those two!

Usually, when we say “automation,” we picture autopilot mode (the tasks Claude runs while we’re sleeping), but, at least for me, automation can also mean collaboration mode (keeping a human in the loop).

Collaboration mode follows the 10-80-10 rule.

The first 10% is yours: the ideas, the direction, and the definition of done. Talk to AI about what you’re trying to accomplish, give it your expertise, and agree on what a finished result looks like. This 10% is your building stage. It might seem small, but it's the foundation everything else rests on.

The middle 80% is AI’s: the execution. The drafting, building, formatting, organizing. The part that takes hours but doesn’t need your taste.

The final 10% is yours again: the review, the personal touch, the judgment call before it ships.

In practice, here’s how this works for my team: whenever a workflow needs human judgment, I assign one person to it. Sometimes I split an automation into two phases, so their phase-1 decisions improve the phase-2 output. Other times, they only weigh in at the very end, right before it reaches the client.

You stay the author. AI does the typing.

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4. Train AI like a new employee

Rome wasn’t built in a day.

If you want to make the most out of AI, you should treat it like a new hire.

You don’t expect a new employee to perform as well as your top people on day one. You ramp them up. You give them context, feedback, and time to learn how you work.

AI is no different.

If you want AI to perform at its best, do what you did with your team on day one.

Give AI:

1. The way you work (guides, SOPs, etc)

2. The templates you use

3. and any document you’ll give to a new hire

The good news? AI learns faster. It can swallow a ton of information at once.

Over time, your SOPs will become Claude skills, and Claude will fill in your templates. With this, AI will be able to handle many of your tasks in one shot.

But don’t expect that to happen on day 1.

To get there, you’ll need time to learn from your own automation and connect the dots.

Here’s the setup I use to train Claude like a new employee.

I create a Claude project for every major task I have. Inside a project, I give access to a folder that contains my onboarding files (md files, templates, etc)

Here’s what this looks:

However, my workspace only looks like that at the end (after weeks of building).

Here’s my typical journey:

• Day 1: Things are messy. I gather the files, but my workspace isn’t tidy

• Week 1: The automation is done, but it’s still not the most optimal solution (doesn’t follow the best practices, not token-efficient, etc)

• Month 1: I optimize the automation, starting from those that are critical

On day one, my AI employee can barely do a thing. By the end of week one, it’s getting real work done (sometimes in one shot), but it’s not yet at its peak. It’s only after weeks of trial and error and connecting the dots that I get its best version.

Why do I work like this?

When I build systems, I don’t care if it follows the best practices. My main goal is to build something that works reliably.

I know this isn’t the right way to do things. However, I prefer to occasionally fix a few things on the fly, rather than get stuck trying to make everything perfect from the start.

Is all this training worth it?

Of course! Just look at customer support.

AI answers questions straight from the help docs. When it gets one wrong, a human steps in, answers the customer, and then fixes the docs. Next time that question comes in, AI nails it.

Every fix makes the system permanently smarter.

Bonus: How to write the instructions for your AI employee

A new hire can’t read your mind. Neither can AI.

You need to break your tasks down into steps and translate your decisions and even your intuition into clear instructions.

Write them down, including your if-this-then-that branches (”if the client replies in under an hour, I prioritize them”).

You could ask Claude: “Which of these steps can an AI do without me?”

The answer will most likely involve steps that don’t need human judgment.

Pro tip: Instead of writing down the steps, record yourself explaining the task (what you're doing and why), then hand the transcript to AI. I do this with the iPhone's Voice Memos app, which generates a transcript automatically. I haven't tried video yet, since Claude can't watch videos. Google Gemini can, though, so I might give that a shot soon (or find a way to connect them)

One more thing

Right now, the distance between people who can hand real work to AI and people who poke at it like a toy is enormous. Everything in this guide is something you can start this week: pick one bottleneck, automate one task, write one context file.

One task. Start small. That’s the whole ask.

If you want the full path (from your first real prompt to systems that run themselves), consider becoming a paid subscriber. You’ll get every deep-dive guide I publish, and your future self, the one who stopped doing the boring 80%, says thanks.

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A year ago, there were barely any guides or video tutorials for Claude.

Today it’s the opposite. You open Google and find dozens of guides and Claude video courses. You watch ten minutes of one, feel a little lost, and close the tab.

A week later, you’re exactly where you started.

I’ve been there, so I did the digging for you. I went through more than 20 free courses and threw out the noise. Then I sorted what’s left by the way you actually like to learn.

Here are the best Claude courses.

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If you like step-by-step guides

If you’re the kind of person who likes to read, follow along, and do it yourself at your own pace, this one’s for you.

The course is split in 3 levels, so you don’t drown trying to learn everything at once. Think of it like a video game: you finish level 1 before you unlock level 2.

Here’s what the levels are about:

Level 1: Claude chat

This is the regular Claude you talk to, the one that looks a lot like ChatGPT.

Most people stop here and never get past the chatbot stage.

You’ll learn Claude features, how to set it up properly, and how to create a simple file that tells Claude who you are and how you work, so you stop re-explaining yourself every single time.

Once you do that, you’re already ahead of most users who just open a blank chat and start typing.

Level 2: Claude Cowork

This is where Claude stops talking and starts doing.

Cowork lives in the desktop app. The big difference: ChatGPT gives you text you have to copy and paste somewhere else, while Cowork creates the actual files for you (the document, the spreadsheet, the styling, all of it).

In level 2, you’ll learn how to set up Cowork, build your first skill, run tasks from your phone, turn Claude into something close to a 24/7 employee and more! For most non-technical people, this is the level that changes how you work.

Level 3: Claude Code

Claude Code is Claude Cowork on steroids.

They made Cowork to break the “I’m afraid of coding” barrier. But once you start hitting Cowork’s limits, Claude Code is how you break through them. And no, you don’t need to know how to code (the name scares people off, that’s all).

Level 3 covers why Claude Code is worth it, what to learn first, how skills work, claude.md and more. For this particular level, I haven’t covered everything yet, so you’ll need to take the Claude Code course below for a deep dive.

You don’t have to reach Level 3 to get a ton of value. But if you want to break Cowork’s limits, this is the door.

How to get my free Claude course

If you’re not a subscriber, click here to get my free Claude course (or leave your email below). After you subscribe, you’ll get a welcome email with the link to the course.

If you’re already a subscriber (and can’t find the welcome email), DM “free“ on Instagram or LinkedIn, and I’ll send you the link (you could leave a comment here, but I don’t get notifications, so I might lose your message)

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P.S. Levels 1, 2, and 3 are free for everyone, but some guides I publish weekly are exclusive to paid subscribers. Support us by becoming a paid subscriber.

If you like video tutorials

Some people learn better by watching someone do it on screen.

Here are three free YouTube courses I’d actually recommend. Each one covers a different piece of the Claude puzzle.

Claude Cowork 60 Minute Masterclass (Sabrina Ramonov)

If you only watch one video, make it this one.

If you want one video to understand Cowork, this is it. Sabrina sets up the Claude desktop app from scratch, then runs five real use cases live: organizing a messy files folder, building an email assistant, creating on-brand content, and automating social media. It pairs perfectly with my Level 2 above.

Watch it, then go build something.

Key timestamps:

• 00:42 Download and install Claude Desktop + interface tour

• 02:10 Chat mode vs Cowork mode vs Claude Code (the difference that confuses everyone)

• 09:48 Skills explained (reusable AI playbooks)

• 19:00 Use case: organizing a messy local files folder

• 30:28 Use case: a personal email assistant with Gmail and Calendar

• 39:05 Use case: creating on-brand marketing content

Watch it here:

Claude Code Full Course, 4 Hours (Nick Saraev)

This one is for people who want to go all in. This is an end-to-end beginner course that takes you from installing Claude Code all the way to shipping (and selling) a real project. Nick covers setup, using the CLAUDE.md file as your project’s brain, building your first web app, then the power-user stuff: skills, sub-agents, and git worktrees to run hours of work in minutes.

Save this one for after you’re comfortable with Levels 1 and 2.

What it covers:

• 03:44 Setting up and installing Claude Code

• 24:01 Building your first web app with Claude Code

• 30:29 Using the CLAUDE.md file as your project brain

• 54:42 Advanced features (hooks, slash commands, the .claude directory)

• 2:35:29 Creating skills to work faster

• 3:11:08 Sub-agents and agent teams (Claude working in parallel)

Watch it here:

Bonus: Claude Design 2 Hour Course (Nate Herk)

Claude can also design. This 2-hour course takes you from zero to shipping real work with Claude Design, the kind of thing you’d normally use a tool like Canva for. Nate builds a full brand called Tally from scratch, so you watch one project come together start to finish.

If you create visuals, slides, or websites for work, this is worth the watch.

What it covers:

• 00:00 What Claude Design actually is

• 11:33 Creating the design system

• 19:50 Building a pitch deck

• 40:45 Building a high-fidelity website

• 57:51 A mobile app prototype

• 1:42:01 Pushing the site live with GitHub and Vercel

Watch it here:

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If you want courses straight from Anthropic

Here’s something a lot of people don’t know: Anthropic, the company that makes Claude, has its own free academy with certified courses taught by the people who actually built Claude.

They’re free and you can get a certificate for LinkedIn.

→ Link to Anthropic courses: anthropic.skilljar.com

Here are the best courses they offer:

Claude 101

If you take only one course off this entire list, take this one.

Claude 101 covers features such as projects, artifacts, skills, connectors, enterprise search, and research mode. These are the features that turn Claude from a chatbot you talk to into something you build real workflows inside. It’s broken into short modules, with use-case breakdowns by role at the end (marketers, writers, analysts, operations). Takes about 1 to 2 hours.

AI Fluency: Framework and Foundations

This course is a way of thinking about how to work with AI effectively and responsibly.

The whole thing is built around four ideas they call the four D’s:

• Delegation: knowing when to hand a task to AI and when to do it yourself

• Description: clearly telling the AI what you actually want

• Discernment: judging whether the output is any good before you use it

• Diligence: the responsibility you still carry even when AI did the work

If you manage a team or you’re the person deciding how AI gets used at your company, this course will shift how you think about it more than any feature tutorial.

The AI Fluency spin-offs: Educators, students, teaching

Anthropic took that same fluency framework and made three more versions for specific groups:

• AI Fluency for Educators: for teachers and anyone designing courses, applied to curriculum design and assessments

• AI Fluency for Students: the framework reframed around studying, learning, and career planning

• Teaching AI Fluency: for people whose actual job is teaching AI fluency to others, like trainers and L&D teams

The advanced track

Here are some courses recommended for those who want to build with Claude

• Claude Code 101: how Claude’s command-line tool fits into a daily coding workflow

• Claude Code in Action: it’s 15 short lectures on real development tasks — navigating a codebase you’ve never seen, planning a feature before writing it, etc

• Introduction to MCP: how to connect Claude to your own tools, files, and databases

One more thing

You don’t need 20 courses.

You need the right one for where you are right now.

And if you want a course that keeps updating, subscribe to Artificial Corner 👇 I release new Claude guides every week.

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Most people think you need a technical background to understand AI.

You don’t.

And you don’t even need to read every research paper. You just need someone to explain what’s actually happening, in plain English.

I read the most relevant AI research papers, so you don’t have to.

I pull out the concepts that actually matter for you as a Claude or ChatGPT user and break them down in a way anyone can follow.

In this guide, you’ll learn:

• How AI reads your prompt, and why your long chats start to fall apart

• Why training data isn’t enough

• Why AI sounds polite, why it agrees too easily, and how to fix it

• Why giving AI more time to think leads to better answers

• What an AI agent really is (and why you need them in 2026)

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1. How AI reads your prompt, and why long chats start to fall apart

As you read this, you go word by word, from left to right.

AI doesn’t work that way.

When you send a message, the model looks at everything at once. It compares words with other words, phrases with phrases, your instructions with your examples, and figures out which parts are connected.

Before this worked the way it does now, systems had to process text in order, one piece at a time. That made it hard to connect ideas that were far apart.

The model that changed all of this is called a Transformer. Instead of reading from start to finish, it looks at many relationships in the text at the same time.

In the past, AI processed language like a slow reader: one word at a time (using RNNs). Transformers made this process faster.

But this comes with a cost.

Comparing everything with everything is expensive. If you double the length of your message, the work almost quadruples. That’s why every AI tool has a limit on how much text it can process at once. This is what people call the context window.

The context window is everything the AI can “see” in an active conversation: your messages, its responses, and the files you uploaded.

When it fills up, it starts losing access to what was said earlier. No warning. It just forgets.

So what do you do?

Put your most important instructions at the beginning of a long prompt (or repeat them at the end). And if the conversation has already gone on too long, start a new one with a quick summary of what was decided.

The goal: keep the context clean. Noise takes up space too, and that affects the quality of the answers.

👉 If you’re curious, the paper is here

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2. Why AI training data isn’t enough

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Most people who want to make money with Claude start in the exact same place. They try to build an app.

They watch someone vibe-code a tool in an afternoon, think “I could sell that,” and spend the next three weekends fighting bugs for a product nobody ends up buying.

Here’s the uncomfortable part: the vast majority of vibe-coded apps never make a single sale.

The people actually paying their rent with Claude are doing something far less exciting. They sell a service, and let Claude do most of the work behind it.

You don’t need to code
You don’t need to be an AI expert
You don’t even need a brand-new idea

You need to be useful to someone who’s willing to pay.

In this guide, you’ll learn:

• The one mindset shift that separates people who earn with Claude from the rest

• Why a service beats an app (almost) every time

• 5 ways people are actually getting paid with Claude right now

• How to turn a single client project into income that repeats

• How to land your first clients (even starting from zero)

Consider becoming a paid subscriber to get full access to all my guides (+ these perks) 👇

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You’re the CEO. Claude is Your Team

Let’s start with the mistake, because almost everyone makes it.

People treat Claude like a magic button. They expect to type one prompt, get a finished business, and watch the money roll in.

Here’s a better way to think about it.

You are the CEO. Claude is your worker.

Actually, it’s a whole team of workers. It can research, write, design, crunch numbers, and handle the boring parts of a job.

But it still needs someone with a vision telling it what to do and deciding what’s good enough to send to a client.

No amount of AI saves a bad leader. That part is still on you.

And here’s the best part:

You don’t have to be technical or an expert in AI.

The most important thing is to be good enough at what you do to tell good output from bad.

I know that sounds too good to be true, but two years ago, almost nobody knew what Claude was (I signed up and never used it!) Everyone you see teaching this stuff today (me included) started from zero a few months/years ago

At the end of the day, people don’t pay for your credentials. They pay for results. If you can deliver the result, and Claude helps you deliver it faster, you’re already in business.

Click here to get my FREE Claude course (20+ lessons)

Stop building apps. Sell a service

Before picking what to sell, let’s kill the most common bad ideas, because chasing the wrong model is how people waste months.

A good way to make money with AI should be cheap to start, have healthy margins, get you to real income relatively fast, and still work a year from now.

Unless you have thousands of followers, I wouldn’t recommend:

• Vibe-coding apps: high margins on paper, but getting actual users is brutal, and someone can copy you next week.

• AI dropshipping: high ad costs, thin margins, a race to the bottom.

• Faceless YouTube channels: everyone uses the same tools, so nothing makes you stand out (and you’re not building anything that’s really yours).

• Cheap templates: easy to make, easy for anyone else to clone

Notice the pattern? They all either cost a lot up front or are trivially easy to copy.

Now the one that quietly wins: selling a service powered by AI.

The math is simple. Startup cost is basically zero, because you sell the service first and deliver it after. Margins are high, because Claude does most of the work instead of a team of employees. And if you wrap it in a personal brand, it gets more durable over time, not less.

My rule of thumb: sell first, build later.

You’re not committing to building anything. You’re making a promise (”I can do this for you, here’s the price”) and only doing the work once someone says yes.

And please, don’t overthink your offer.

Some people spend dozens of hours agonizing over the perfect offer and pricing, then a few hours learning Claude, then almost no time actually selling.

That’s backwards.

Spend around 30 minutes on your offer. Pick something, slap a price on it, and move.

You’ll change it anyway once the market tells you what it’ll pay (you might find out you were charging too little).

If you’re staring at a blank page, use a simple fit test to decide what to sell. Look for the overlap of 3 things:

1. What you love doing

2. What you’re actually good at

3. What people will pay for

The sweet spot is where all 3 meet. That’s your offer.

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5 ways people are getting paid with Claude

Here are the ones that work right now. Pick the one closest to what you already know. The ones needing deeper Claude knowledge are at the end.

1. Sell a skill you already have, powered by Claude

Copywriting, SEO, marketing, thumbnails, admin work, social media, etc.

These services existed long before AI. The difference now is that Claude does 80-90% of the work, so you can charge a normal rate and keep most of it instead of hiring a team. You don’t need a new skill. You need to do an old one faster.

2. Teach Claude one-on-one, to teams, or small businesses

Everyone wants to learn Claude right now. You offer training for a team, set a price, set a length, list the topics. It can be a workshop, one-on-one coaching, a course, or a paid community. Pick a format and go.

Do you need to be an expert?

Actually, no. You just need to:

• Learn Claude well enough to teach the basics

• Have built something where you actually applied those basics

With that, you can sell training, coaching, or a small course to people and teams who feel behind.

“Teaching“sounds like something exclusive to educators or AI experts, but it’s not!

Believe me, you only need to be a few steps ahead of your students, and that’s enough.

3. Fix the boring data nobody wants to touch

As a data analyst, I can tell you this — most companies sit on a mess: spreadsheets everywhere, old systems that don’t talk to each other.

And you can’t put AI on top of broken data. It just doesn’t work. So someone has to clean it up first.

The solution? You connect Claude to their data and do the cleaning so they can finally ask questions in plain English and get real insights. Here, the biggest opportunity is with companies that are comfortable sharing data with AI (mostly small businesses).

4. Be the one-person agency

Not long ago, helping a business launch meant hiring a designer, a copywriter, a developer and other professionals.

Now one person with Claude can do almost everything: a landing page, an email funnel, a simple chatbot, and the ad campaigns.

I put this one almost at the end because it’ll require you to create systems that run without your intervention. Your work? Feed the system, push back when Claude sounds off, and tell good output from bad.

You might still need to hire a freelancer to pick the best of what AI produces (for fields you have little knowledge), but it costs a fraction of what you’d have paid before.

5. Become an AI consultant for small businesses

Every small business owner in your town is quietly panicking about AI. They have no idea where to start.

So you walk in, spend a couple of hours understanding their business, feed it all into Claude, and hand them a clear roadmap: the top moves ranked by what makes them the most money, plus a 30-day plan.

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How to land your first clients

This is probably the most important part.

You can have the best offer in the world and still make nothing if nobody knows you exist. There are two ways to fix that, and they work best together.

Way one: cold outreach.

This is the best path to cash in the short term.

You reach out directly to people who might need your service. Go find people who are clearly interested (anyone commenting “teach me Claude” under a popular post is a warm lead) and message them. But don’t open with “buy my thing.” Lead with value. Start a real conversation, and you can mention your paid help at the end.

Do this by hand at first. Don’t automate it before you know what actually works. Test your messages. Try one version on your first batch, a different version on the next, and keep whatever gets replies.

You can also reach out to businesses (and Claude does the hunting)

Here’s one idea: pull business data from Google Maps. Point Claude at Google Maps or a directory and have it build you a clean spreadsheet of local businesses, with names and contact info, ready to reach out to.

Lead gen that used to take a full day now takes minutes.

Way two: post content so clients come to you.

This is the long game, and it’s the one I’m most bullish on.

If you can build even a small audience around Claude, it sets you up for the rest of your career, whatever you decide to sell next. You post about what you’re learning, people see you as “the AI person,” and some of them start DMing you to work together.

But here’s an honest take. Getting views on social media is not easy.

If I had to start over, I’d avoid YouTube, LinkedIn, and Instagram. They’re hard (even for me now!). The best platforms for new creators are TikTok and my recent discovery, Threads. Both still give you views regardless of how many followers you have. I started with 0 followers one week ago on Threads, and now I have almost 1k followers (all organic)

Here’s one idea (that works for me): Find content that’s already going viral about Claude/AI and make your own version. Copy the structure, the hook, and then say it in your own words.

Pro tip: Turn one project into income that repeats

Landing a client is great. But one-time projects mean you’re always hunting for the next one. The real goal is income that repeats.

The first move is simple: don’t end the relationship when the project ends.

After you deliver, stay on as their ongoing “AI advisor.”

For an audit client, that might be a few thousand a month to keep their roadmap fresh. For a tool you built, it might be a smaller monthly fee to maintain it and add features.

The second move is the one that quietly changes everything: productize your work.

Every project you finish has reusable parts: the template, the system, the setup. Polish those parts, record a simple walk-through, and sell it as a product. You built it once, for a paying client. Now you sell it again and again to people with the same problem. Of course, you won’t always be able to sell it as is. You might need to adapt it to each business context, but you’re not starting from scratch anymore.

One more thing

Don’t quit on day three. Most people look for clients or post once, hear crickets, and give up. The ones who win just keep showing up. Give it a real shot, say the next 90 days, before you decide it doesn’t work.

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They think the next model, the next AI tool, the next setup is the thing standing between them and real results.

Actually, they have a skill problem.

The people getting incredible output from AI aren’t using secret tools. They’re using the same Claude and ChatGPT as everyone else. They’ve just gotten good at a handful of skills that the tools can’t give you.

Those skills are what this guide is about. Seven of them, from beginner to pro.

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Four years ago, I built a simple Python model to predict the 2022 World Cup.

I wrote every line of that old model myself. I didn’t build a sophisticated model, but the project still took me weeks.

This time, I didn’t write a single line of code. I told Claude what I wanted in plain English, and it pulled the data, built a smarter model, tested it on the last two World Cups, and even made the charts you’re about to see.

I just made the calls on what to build.

Here's why this matters to you, even if you think soccer is the most boring thing on earth. Swap "49,000 soccer matches" for "five years of your bank statements" or "that sales export you've never had the time to open." Same machine.

The hard part used to be writing the code. Now it’s knowing what to ask for, stress-testing a plan, pushing back, and double-checking the outputs.

The World Cup is just a fun way to show you how that works.

Oh, and one more thing: this is the biggest World Cup ever. 48 teams, kicking off June 11th across the US, Canada, and Mexico (the US is hosting 78 of the 104 games!).

Let’s predict the winner with Claude.

In this guide, you’ll get:

• How Claude’s prediction model works (in plain English)

• The favorites, the most likely bracket, and the winner

• What this Claude project means for you (even if you hate soccer)

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How Claude’s prediction model works

Here’s what I had Claude do, in four steps.

1. Every game, not just World Cup matches: I had Claude pull every men’s international since 1872. That’s about 49,000 matches, including all the friendlies and qualifiers my old model skipped. Recent games count more and older ones fade out (a game from last month matters way more than one from 2014).

2. Rate every team: I asked Claude to give each team a strength score using something called Elo. If you’ve ever played chess online, you’ve seen it: win and your number goes up, lose to someone weaker and it drops a lot. Same idea, just for national teams.

3. Turn the ratings into a score: Claude turns the gap between two teams into a likely scoreline, with a bonus for the three hosts playing at home.

4. Play the World Cup 50,000 times: Instead of guessing one bracket, I had Claude play the entire tournament 50,000 times and count how often each team won. That’s where the odds come from.

If you want to dive into the more technical details, I left it all in the "Under the hood" section at the end.

I tested it on the World Cups 2018 and 2022 first

Before trusting the model, I had Claude run it on the last two World Cups, using only the data that existed before each one kicked off.

2022 went well. The model made Argentina a co-favorite, second only to Brazil, and Argentina won. My old model’s one and only pick was Brazil, who went out in the quarters. Having the real winner sitting near the top is a much more useful answer.

2018 was a miss. Why? The model loved Brazil and Germany. France won. It had France down in fifth.

Here’s the thing, though: soccer is genuinely hard to predict. Across all those games, the model called the right result about 52% of the time, which is actually decent for a sport with this many upsets. The new model isn’t trying to be magic. It’s trying to give honest odds instead of one overconfident guess.

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The favorites

Two teams stand above everyone else.

Spain comes out on top at about 28%, with Argentina right behind at 21%. Together, that’s almost half the field’s chances sitting with just two teams. Then there’s a cliff. France is the best of the rest at 11%, and after that, England, Brazil, Colombia, and Portugal are all bunched in the mid-single digits.

Here’s the full top 10, with the odds of winning it all and of going deep into the tournament:

The bookies also have Spain on top, so we agree on the headline.

The difference is they spread the love around more and rate France and England higher than I do (well, than Claude does). My model is just more stubborn about Spain and Argentina, because on recent results those two have been ruthless.

One more way to look at it. The darker the box, the more likely a team makes it to that round:

The most likely bracket (and the winner)

Put it all together and you get the single most likely path through the World Cup.

The model sends Spain and Argentina to the final, with France and England losing in the semis. And the team it has lifting the trophy at MetLife Stadium on July 19?

Spain

Will it play out exactly like this? Almost certainly not (that’s the whole reason I ran it 50,000 times instead of once). One bracket is just the most likely story out of millions.

What this means for you (even if you hate soccer)

Here are a few lessons to keep in mind for your next Claude project:

1. Your messy data is finally useful

The hardest part of a data project always used to be the data. Here, Claude chewed through 49,000 matches without blinking. For you that pile could be five years of bank statements, a folder of PDFs, or a sales export you’ve never had the time to open.

Point Claude at it, ask it to clean, generate charts, etc. I did this using Cowork, but you can also use Claude in Excel.

2. Stay in the driver’s seat

This wasn’t me firing one prompt and walking away.

Before Claude built anything, I made it show me the exact data it pulled, double-checked it myself, and grilled it on one specific choice: should a game from 1954 count as much as one from last year?

Only once I liked the answer did I tell it to keep going.

That back-and-forth is the actual job now. You supervise, you question the parts that smell off, and you approve the next step. Claude does the building, you make the calls (this beats someone who just types “predict the World Cup” and hits enter).

3. I got lucky with the data. You might not

Honest caveat. This went smoothly partly because a clean dataset was already sitting on Kaggle, ready to download. A lot of real tasks won’t be that tidy. The numbers you want might be scattered across a few websites. Ask Claude to go scrape them for you. Don’t expect every data project to be this clean. Expect to wrangle.

4. Ask Claude where it’s weak

My favorite one. I didn’t have to hunt for the flaws myself. I just asked Claude to test its own work, and it told me straight: it would have missed the 2018 winner, and it disagrees with the bookmakers more than I’d like.

That honesty is there, but you have to ask for it. On your next project, don’t stop at the polished answer. Ask “where is this wrong?” and “what are the weak spots?” A good answer that knows its limits beats a confident one that’s hiding them (you can even bake that habit into a skill)

Your turn: can you beat Claude?

The fun part starts now.

Claude’s model says Spain. But we both know soccer’s whole personality is making models and analysts look silly. It happens almost every tournament.

So I want the human take. Yours.

Who's lifting the trophy? Drop your pick in the comments (and throw in the final score if you're feeling brave).

On July 19th, we’ll see who called it better: the model, or you.

Leave a comment

One more thing

Anyone can type “predict the World Cup” and hit enter.

Getting a result you’d actually trust is a different thing, and that’s the part I keep digging into. How to point Claude at your own messy data, question the parts that smell off, and walk away with something ready to use instead of slop.

That’s what I share with paid subscribers.

Not my soccer takes :) but step-by-step guides that will help you unlock Claude’s full potential for your own work. If you want to get access to all my guides, consider becoming a paid subscriber 👇

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Let’s connect! You can find me on LinkedIn, Instagram and X

P.S. Here’s how the model works under the hood

Bonus: How the model actually works (skip this if you’re not technical)

Here are the same four steps again, this time with the real math, a few actual numbers, and a bit of the code Claude wrote. My old 2022 model leaned on one idea: the Poisson formula, a simple way to turn an average number of goals into the odds of each scoreline. I kept that part (it still works). I just fixed what feeds into it.

1. Strength, with Elo

Every team starts at 1500 points. After each game the winner takes points from the loser, and how many depends on how surprising the result was, the winning margin, and how big the match was (a World Cup game counts for more than a friendly).

The expected result before kickoff is one line:

expected = 1 / (1 + 10 ** ((opponent_rating - team_rating) / 400))

Then both ratings update:

k = importance * margin_bonus          # bigger game + bigger win = bigger swing
elo_home += k * (result - expected)    # result = 1 win, 0.5 draw, 0 loss
elo_away -= k * (result - expected)

Old games fade on their own here, because every new result writes over the old rating.

Run that across all 49,000 matches and you get a strength number for every nation.

Here’s the top of the table going into 2026:

No big surprises at the top, which is a good sign the ratings aren’t junk.

2. Recent games count more

This is the fix for my old model treating 1954 like yesterday. When the model learns how a rating gap turns into goals, it leans on recent matches and lets old ones fade, cutting their weight in half every 2.5 years. A game from the 2022 World Cup is worth about 0.4 of a brand-new one, a 2018 game about 0.1, and anything before 2010 is basically zero. I didn’t pick that 2.5-year number by feel. I had Claude test a whole range of values and keep the one that predicted past results best.

3. From two ratings to a scoreline

Two ratings go in, a likely score comes out. Claude fit a small formula on those recent games that turns the rating gap into an expected number of goals for each team:

expected_goals = exp(0.17 + 0.19 * (rating_gap / 100) + 0.17 * home)

Feed both teams’ expected goals into the Poisson formula (the same one from my 2022 article) and you get the odds of every scoreline: 1–0, 2–1, 0–0, and so on. Add them up and you get the win, draw, and loss chances.

Spain vs Brazil on a neutral field, for example, lands around Spain 53%, draw 25%, Brazil 22%.

4. Play it 50,000 times

This is the real upgrade over 2022. For each simulated tournament, Claude:

• plays all 72 group games by rolling actual scorelines, then ranks each group on points, goal difference, and goals scored (the real World Cup tiebreakers)

• takes the 8 best third-place teams and drops them into the bracket using FIFA’s official rules for the new 48-team format

• plays the knockouts, where a tie goes to extra time and penalties, so the stronger team gets a small edge instead of a pure coin flip

Run that 50,000 times, count how often each team lifts the trophy, and those counts become the title odds.

That’s the whole machine. Strength, recency, a scoreline formula, and a lot of simulated tournaments.

This guide was written in collaboration with Gencay.

Gencay writes LearnAIWithMe, a newsletter focused on practical AI tutorials for people who’d rather replace than be replaced by AI.

In a previous guide, I showed you how to connect NotebookLM with Claude.

It worked, but there was a catch: the whole setup lived in the terminal. CLI, install commands, config. Powerful, but for most people, too technical.

This time, there’s no terminal (or Claude Code).

I found a NotebookLM skill that runs inside Claude Cowork. You only need to download it and add it to Cowork.

In this guide, you’ll learn:

• How to set it up in two steps

• Use case 1: Interview prep that knows a company’s last twelve months

• Use case 2: A weekly language feedback loop that drills your real mistakes

• Use case 3: Pre-trip destination briefings grounded in fresh sources

Use case 2 is connected to my Claude for Foreign Language Learning video course, while use case 3 is related to the AI Travel Guide I published last week.

LearnAIWithMe

If you want to build with AI, not just learn how to prompt, this is the right place.

By Gencay

The Claude Skill

This skill was created by Paul J Lipsky.

Download the skill clicking here. Then open Cowork and follow the steps below.

Customize → Skills → “+” → Create skill → Upload a skill → Upload the skill file

Now I can run NotebookLM through Cowork.

But I got errors the first time, because I skipped half the video.

So before you start, here is what I wish someone had told me.

How to set it up

Two steps. Skip either, and the skill will fail on first run.

Step 1: Install Claude Chrome Extension

Go here and click Add to Chrome.

Step 2: Add the NotebookLM domain to Claude in Chrome

Open the Claude desktop app. Click your name in the bottom left. Hit Settings.

In settings, scroll to Claude in Chrome (Beta). Click Add websites. Type notebooklm.google and confirm.

Without this, the skill cannot drive NotebookLM. The extension is loaded but blocked. That was my first error.

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How to use it?

In any Cowork chat, type:

/notebooklm

The skill loads and asks what you want to do.

Four options come up.

1. Read / extract from a notebook

2. Add sources to a notebook

3. Generate a Studio output

4. Create a new notebook

Let’s pick Create a new notebook.

Question 1: Name

The skill asks if you want to name it yourself or let NotebookLM auto-name it. I named mine Prompt Engineering Course.

Question 2: Sources

The second question will ask you about the sources.

I clicked Yes, add sources now.

Five options: URLs, YouTube, uploaded files, copied text, or something else.

I dropped into the Anthropic prompt engineering course on YouTube:

Next, it loaded the source and took control of my Mac.

A red border appears at the top of the screen.

That is Claude debugging the browser. You watch it click, type, wait, screenshot. Same way you would do it, just faster.

It pasted the YouTube link into the source box.

NotebookLM ingested the transcript and auto-generated a summary describing the video as a roundtable with Anthropic experts on prompt engineering.

Done. Notebook live.

Audio Overview, Mind Map, Reports, Quiz, Infographic, all one click away in the Studio panel.

So here are 3 use cases I love.

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Use Case 1: Weekly Job Interview Prep

The week before an interview, most people do the same thing. Open the company website, read the About page, and maybe check LinkedIn. Walk in underprepared and get caught off guard by something you should have known.

The fix is better synthesis of more sources than you can read.

Step 1. Train NotebookLM with Deep Research

In Cowork, type:

/notebooklm

Create a new notebook called “Stripe Interview”. Then run Deep Research with this prompt:

I have an interview at Stripe. Find sources that cover:

• Company history, mission, and recent strategic moves

• Latest product launches and roadmap signals from the last 12 months

• Public statements from the CEO and senior leadership (podcasts, conferences, X)

• Financial position, fundraising, or earnings if public

• Competitor landscape and how Stripe positions against them

• Glassdoor reviews, common interview formats, and what current employees say about culture

Prioritize sources from the last 12 months. Mix press releases, podcast transcripts, news articles, official advisories, and employee reviews.

Cowork takes over the browser. Red border on top. Opens NotebookLM, creates the notebook, clicks Discover sources, pastes the prompt, and hits run.

After some minutes, the research was done.

And the notebook is ready.

Step 2. Cowork Setup

Create a folder on your computer called interviews. Inside, one subfolder per company.

The artifacts folder holds NotebookLM outputs.

Give Cowork access to interviews.

The notebook from Step 1 holds the knowledge. The folder is the landing strip for what comes next.

Step 3. Generate Artifacts

In the same Cowork chat, type:

/notebooklm

Open the “Stripe Interview” notebook. In the Studio panel, generate:

1. A Briefing Doc covering company narrative, recent strategic priorities, and leadership focus areas

2. A Quiz with likely interview questions, each anchored in something the company has publicly said

3. An FAQ on common pitfalls from Glassdoor reviews and negotiation pressure points

Save all three to /interviews/stripe/artifacts/.

And generate an artifact with three tabs;

1- Briefing Doc

2- Quiz

3- FAQ for common pitfalls

And it started building.

Here is the artifact after a few adjustments.

Step 4. Schedule

Turn it into a habit. Every interview is prepped automatically.

Create a file called /interviews/upcoming.md. Drop your upcoming interviews in it like this:

- Stripe, 2026-07-15

- Anthropic, 2026-07-22

- Notion, 2026-08-03

In Cowork, open Schedule. Create a new task with this prompt:

Every morning at 9 AM, check /interviews/upcoming.md for any interview 7 days away.

For each interview 7 days out:

Create /interviews/{company}/artifacts/.

Use /notebooklm to create a notebook named “{company} Interview”.

Run Deep Research with this prompt, replacing the company name: “I have an interview at {company}. Find sources covering company history and strategy, latest product launches and roadmap from the last 12 months, public statements from leadership, financial position, competitor landscape, Glassdoor reviews, and interview formats. Prioritize the last 12 months. Mix press releases, podcasts, news, and employee reviews.”

Generate Briefing Doc, Quiz, FAQ. Save to /interviews/{company}/artifacts/.

And create an artifact with three tabs based on this briefing.

Set frequency to daily, 9 AM.

That is the whole thing.

You walk into an interview knowing the company’s last twelve months better than half the people already working there.

The next two use cases are exclusive to paid subscribers

• Use Case 2 — Weekly Language Feedback Loop: turn Claude + NotebookLM into a tutor that drills your real mistakes every week. This is just one lesson from my full video course on learning a foreign language with Claude. The moment you upgrade, you’ll receive an email unlocking free access to this and other courses.

• Use Case 3 — Pre-Trip Destination Companion: most AI travel advice is stale, pulled from old training data. This builds a brief from fresh sources, so you walk into any city this summer knowing what's actually true today. You’ll also get my complete guide on using AI to plan trips.

Become a paid subscriber and you get both use cases, the full language course, and the AI travel guide, everything you need to learn a language and prepare for your summer trip.

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Click here to get my FREE Claude course (20+ lessons)

ChatGPT chats. Claude works.

That sounds like a small difference. It’s not.

I used ChatGPT for almost everything in 2023 and 2024. It answered my questions, wrote my drafts, helped me think out loud. But every output stayed stuck inside the chat. I still had to copy, paste, format, and do the actual work myself.

Then I started using Claude Cowork (not the chatbot, the version that lives on your desktop and acts on your computer).

It doesn’t just tell you what to do. It does it: organizes your files, builds your slides, runs tasks while you sleep, and connects to the tools you already use.

I’ve spent months testing the features ChatGPT simply can’t match.

Here are 7 amazing things Claude can do that ChatGPT can’t.

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1. Claude Cowork can do the work (not only chat)

Most people use AI like it’s a search engine. You type something, get an answer, start over.

Claude Cowork works differently.

It’s built for doing tasks: organizing files, creating on-brand content (slides, reports, infographics, etc), and almost anything that can be done on your computer.

You’re actually giving AI a job.

If you’re new to Claude Cowork: Download Claude for desktop, install it, and open the app.

Claude Cowork can review files, follow instructions, keep your project context in mind, generate real deliverables, and handle processes that would be a nightmare to do manually.

ChatGPT chats. Cowork gets things done.

In the video below, Claude Cowork takes a messy folder of receipts and turns it into an organized system: category folders and a summary spreadsheet.

We’ll see many features exclusive to Claude Cowork in the following points.

2. Claude Skills: It turns repeated tasks into reusable workflows

A prompt can solve a task once.

When you do the same task frequently, you end up rebuilding the process every single time.

You explain again what role Claude should take, what criteria to follow, what format you expect, what mistakes to avoid, and what a good result looks like.

That works once. But it doesn’t scale.

That’s the first major shift Claude skills introduced.

Here’s what I got when I asked Claude to create a presentation (no skill enabled):

Here’s what I got when using Anthropic’s brand guidelines skill:

With a slide deck builder skill:

With skills, you teach once and Claude follows your instructions every single time.

To download the best Claude skills, read my guide below

📚 We Tried 100 Claude Skills. These Are The Best

Note: Skills is in beta for a small group of ChatGPT users (no Plus and Pro ChatGPT accounts)

3. You can automate your life with Schedule Tasks

Lately, Claude has been running tasks on my computer while I sleep.

How? With Scheduled Tasks.

Here’s an example.

I used to go to YouTube and manually search for videos on specific topics (part of my research step when writing).

It was tedious.

So I scheduled a task that runs at 3 am and creates a filtered report of trending videos automatically. That alone saved me several hours.

Here’s how to create a scheduled task from scratch:

1. Download Claude for desktop

2. Open the app and choose “Cowork”

3. Click “Scheduled” in the left sidebar

4. Click “+ New task”

5. Fill in the details (prompt, task name, etc.)

6. Click “Save”

Once saved, you'll see a confirmation like this:

That’s the most basic way to create scheduled tasks. For more use cases, read my complete guide below.

📚 Claude Scheduled Tasks: Complete guide

ChatGPT also has Tasks, but they’re mostly for reminders and recurring prompts.

In Cowork, scheduled tasks feel closer to real work automation: files, reports, folders, desktop workflows, recurring tasks Claude keeps handling over time.

Not every task needs you sitting in front of a screen. Some work can run later. Some can repeat every day.

4. Claude connectors

Claude can also move closer to where the work actually happens.

Your work lives in documents, spreadsheets, emails, calendars, and the tools you use every day. Not inside a conversation.

If Claude can’t get closer to that context, you end up doing the bridge work yourself. And that limits how much you can actually delegate.

Connectors bring Claude closer to where your work actually lives.

They bring Claude closer to your Google Drive documents, Gmail, calendars, etc.

To install a connector, go to Cowork → Customize → Connectors → + → Browse connectors

Less manual searching, copying, and pasting before asking for help.

Remember: Skills tell Claude how to work. Connectors give Claude access to where the work lives.

📚 Claude connectors: Complete guide

5. Claude in Excel, Word, and Power Point

A lot of professional work still lives in Office files: spreadsheets, documents, presentations, tables, reports, dashboards.

Copying a formula from a chat and pasting it into Excel gets old fast.

Claude in Excel changes that.

Instead of working from a chat bubble, Claude works closer to the spreadsheet itself: cleaning, analyzing, visualizing, and presenting the data.

In the Claude in Excel: Complete guide, I show examples of how Claude cleans data, summarizes, creates visuals, and synthesizes insights.

Thousands of rows. Manually exploring it would take hours. Claude summarizes the data, identifies trends, and builds a dashboard with visuals inside Excel. After a few tweaks, that raw spreadsheet becomes something you can actually present.

In Word and PowerPoint, same idea. You work closer to the actual file instead of bouncing between a chat and your document.

6. Claude can connect to many AI tools

Claude works well with other tools. Some have an official connector. Others don’t, but there are still useful workarounds.

The easiest way to understand this is with two examples: ElevenLabs and NotebookLM.

Let’s start with ElevenLabs.

ElevenLabs is useful when you need to do text-to-speech, voice cloning, AI dubbing, and audio generation.

Claude helps you write, structure, rewrite, or adapt the script. ElevenLabs then turns that script into a voice output. Good for videos, tutorials, product demos, explainers, podcast-style content, or social media clips.

To use it, just install the ElevenLabs connector.

NotebookLM is different.

It doesn’t have a native Claude connector, but it can still fit into a useful workflow.

NotebookLM is great at organizing knowledge from your documents and sources. Claude can take that knowledge and turn it into something more actionable: reports, infographics, dashboards, apps, working materials.

Here’s a workaround to connect Cowork with NotebookLM:

1. Run this prompt in Cowork:

Install this skill :https://github.com/teng-lin/notebooklm-py
I want to use it via CLI

2. Ask Cowork to turn it into a Skill.

3. Authenticate your Google account from the terminal with notebooklm login.

4. Copy the `storage_state.json` pat back into Cowork.

5. Test the Skill by typing `/notebooklm` and asking Claude to list you notebooks.

Once it works, Cowork can reach your NotebookLM sources.

I tested this with a messy retail sales spreadsheet.

NotebookLM pulled out the five most important insights from the data, with citations back to the source. Then Claude Cowork used those insights to build a one-page infographic app with different visual styles and a PNG export button.

That's the workflow: source material → grounded insights → usable output.

For more, read the complete guide below.

📚 Claude + NotebookLM: Complete guide

7. Claude Cowork scrapes websites reliably

ChatGPT can browse the web and pull information from pages. But web scraping needs more than browsing.

You need fields, filters, edge cases, repeated runs, and structured outputs.

Claude has two ways to handle web scraping.

Option 1: Claude Cowork (the basic way)

Cowork can visit a page, read the content, extract the fields you need, and turn the result into a dataset.

I used it to scrape my own site archive: date, post text, likes, comments, restacks, and post link.

The first run wasn't perfect. I gave Claude feedback and corrected the extraction logic. Once it worked, I scheduled this task to run every month.

Here’s what the output looks like.

Option 2: Claude Cowork + Apify (the method that scales)

Apify gives Claude access to ready-made scrapers for more complex websites and data sources. Better for robust, scalable scraping.

One example: a Reddit Trend Tracker.

Instead of manually checking Reddit to understand what people are talking about, Apify pulls structured data from relevant communities. Cowork then turns that into trends, summaries, or research material.

For more, read my complete Claude + web scraping guide below.

📚 Claude web scraping: Complete guide

Bonus: Claude can generate chats and artifacts

Not every output should stay as text. Sometimes you want something you can open, review, show, or use directly.

Claude can create three useful types of outputs for that: Charts, Artifacts, and Live Artifacts.

Each one solves a different problem.

Charts

Useful when you need to turn information into a cleaner visual: graphs, comparisons, dashboards, visual explanations. For example, you can ask Claude to turn content performance data into an interactive chart.

Use this prompt:

Create an interactive chart from this data.

Data:
- LinkedIn post: 18,000 views, 760 clicks, 120 subscribers
- Substack article: 7,500 views, 980 clicks, 210 subscribers
- X thread: 12,000 views, 430 clicks, 80 subscribers
- YouTube short: 25,000 views, 510 clicks, 95 subscribers
- Carousel: 9,800 views, 690 clicks, 150 subscribers

Make it explorable, not just static.

Include:
- hover details for each content format
- highlighted insights for the best format by metric
- clear labels
- a short explanation of what the chart shows
- toggles to compare views, clicks, and subscribers

Then you get a chart you can explore instead of a flat table:

Artifacts

Useful when the output needs to work like a small tool: a calculator, a dashboard, a page, a comparator, or a simple interface.

Here’s a prompt example:

Build an interactive calculator for estimating article production effort.

Use this case:
- Article type: long-form guide
- Research time: 3 hours
- Writing time: 4 hours
- Editing time: 2 hours
- Visuals: 3 visuals
- Publishing format: Substack + LinkedIn repurpose

It should include:
- editable inputs
- total estimated hours
- difficulty level
- recommendation
- warning if the workload is too high for one day

Make it clean, simple, and easy to use.

Build this as an interactive Artifact.
Make it usable, not just explanatory.

Then Claude creates something you can interact with, not just read:

Live Artifacts

Live Artifacts go one step further.

Useful when you need a tracker, monitor, dashboard, or workspace you can return to inside Claude Cowork. For example, you can create a live AI video radar that tracks videos about a specific topic.

Use this prompt:

Create a live AI video radar inside Claude Cowork.

Goal:
Track recent YouTube videos about AI, Claude, Claude Code, AI productivity, and workflow automation.

Use official or authorized sources when possible, such as:
- YouTube Data API
- channel RSS feeds
- exported CSV files
- manually pasted video links

Track:
- video title
- channel
- publish date
- views
- likes
- comment count
- duration
- topic category
- why it matters
- possible article angle
- possible LinkedIn post idea
- possible X post idea
- next action

Start with these search topics:
- Claude Code
- Claude Artifacts
- Claude Cowork
- AI productivity
- AI agents
- prompt engineering

Make it easy to refresh weekly.

Highlight:
- videos growing fast
- repeated topics across channels
- content gaps I could write about
- videos worth watching first

Then you get a workspace you can reuse, update, and review over time:

For more about charts and artifacts, read my guide below.

📚 Claude Charts, Artifacts: Complete guide

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So why do you need skills for Claude Code?

If you’ve been using Cowork a lot (like me), you’ve probably hit a wall already. Cowork has limits that keep it safe for non-technical folks. That’s a good thing most of the time. But every now and then, you want to do more than it allows.

That’s when you need Claude Code.

And no, you don’t need to be an expert programmer to use it. Especially if you have the right skills installed.

When you build something with code, half the job isn't writing it. It's making sure it actually works: catching bugs, testing it, not shipping something that breaks the second someone uses it.

That's the part that scares people off (and what takes real experience).

These skills do that part for you! Claude plans it, tests it, and reviews its own work. You just steer.

In this guide, you’ll get:

• How to quickly install a Claude Code skill

• The best 6 Claude Code skills

• The exact command to install each one

Every week, I publish new Claude guides: some free, some paid.

Click here to get my FREE Claude course (20+ lessons)

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How to Install a Claude Code Skill

This part scares people off for no reason.

Installing a skill is usually one command pasted into your terminal.

Most skills live in something called a "marketplace" (think of it like an app store for Claude). The official ones are already connected, so you just install.

Here’s how to install one:

1. Open Claude Code in your terminal.

2. Paste the install command for the skill you want (I list each one below).

3. If a skill lives in its own “marketplace,” add the marketplace first, then install it.

4. Restart Claude Code, so it picks up the new skill.

Here’s an example:

That’s it. Most skills install in under a minute.

Pro tip: install the ones you’ll use everywhere (like Skill Creator below) “globally,” so they work in every project without you having to think about it.

The best 6 Claude Code skills

1) Skill Creator

Problem:

Most people who try to build their own skill hit a wall fast. They don’t know the structure, the formatting, or what makes a skill reliable. So they either give up or build something that breaks the second a client touches it.

The skill:

skill-creator is the official Anthropic skill that builds other skills. You describe what you want in plain English, like you’re explaining a task to a coworker. Claude drafts the skill, tests it, fixes it, and packages it into something you can reuse forever.

Why it works:

Every custom skill you make later comes out of this one. It compresses the whole learning curve from "I have no idea how skills work" to "I just made one in five minutes"

This is the first one I'd install. Here’s the command to install it:

/plugin install skill-creator@claude-plugins-official

2) Grill Me

Problem:

Claude loves to jump straight to a plan. You give it a half-formed idea and it spits out a full document before it actually understands what you want. Then you spend the next hour correcting it.

The skill:

This one comes from Matt Pocock and it’s one my favorite skills. It tells Claude to interview you relentlessly about your plan until you both actually understand it, working down every branch of the decision before writing a single line of code. In one session, it asked Matt 16 questions. On bigger features, he’s had it ask 30 to 50.

Why it works:

Most mistakes come from Claude guessing what you meant. Grilling kills the guessing.

Here’s the command to install it:

npx skills@latest add mattpocock/skills -s grill-me -g

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3) Superpowers

Problem:

You ask Claude Code to build something. It sprints out of the gate, writes the whole thing in one shot, and on the surface it looks great. Then you actually run it. Or worse, your client runs it. And it falls apart. Why? Claude doesn’t plan. It doesn’t test. It doesn’t double-check its own work. It just writes.

The Skill:

It forces Claude to work like a senior developer. Step back and plan the whole thing first. Work in an isolated environment so nothing breaks your main project. Write tests before writing code. Then review its own work twice. Once for “does this match what I asked for” and once for code quality.

Why it works:

Most failures with Claude Code aren’t from Claude being dumb. They’re from Claude rushing. Slowing it down just enough to actually think gets your first pass to 80% instead of 60%. That means fewer debugging cycles, lower token costs, and code that doesn’t fall apart the second a real person uses it.

Here’s the command to install it:

/plugin install superpowers@claude-plugins-official

4) Frontend Design

Problem: When Claude builds a website or a slide deck on its own, it often looks AI-generated. Generic, a little off, the kind of thing you’d want to redo before showing it to anyone.

The skill: Frontend Design is the official Anthropic skill for making the things Claude builds actually look good. Install it, and whenever you’re designing something in Claude Code, the output comes out looking a lot less “an AI made this.”

Here’s the command to install it:

/plugin install frontend-design@claude-plugins-official

5) Context Mode

Problem:

Use Claude Code for a while and you’ll notice this: Things get sluggish, it forgets what file you were editing, it asks what you wanted to do (again). What’s happening is every command Claude runs dumps a pile of raw output into its memory. After about 30 minutes, most of that memory is junk. Claude has to reset to keep going. And when it does, it forgets where you were.

The Skill:

It does two things. First, it filters the junk before it ever reaches Claude’s memory. Only the useful part of each command makes it through. Second, it keeps a running log of your session (files edited, tasks in progress, your last prompt). When Claude resets, it pulls that log back in and picks up exactly where you left off.

Why it works:

Most “long sessions” with Claude Code aren’t really long. They’re a bunch of short ones glued together with you re-explaining yourself. This kills both problems at once: less garbage going in, full memory of what you’ve done. Sessions that used to die at the 30-minute mark now run for hours.

Here’s the command to install it:

/plugin marketplace add mksglu/context-mode

/plugin install context-mode@context-mode

6) Claude Mem

Problem:

Claude Code starts every new session from scratch. So you end up explaining the same project over and over again, wasting time and thousands of tokens just catching Claude up.

The skill:

This skill carries your knowledge across all your future sessions. It hooks into Claude’s session and automatically captures what happened: the file edits, the decisions, the bug fixes. It compresses all of that into summaries and stores them locally. When you open a new session, the relevant parts get pulled back in automatically. It even writes and updates your project notes for you while you work.

Here’s the command to install it:

/plugin marketplace add thedotmack/claude-mem

/plugin install claude-mem

Bonus: /review and /ultra-review

Problem:

Even careful work has bugs Claude misses on its own. You need a second set of eyes before you ship something that actually matters.

The skills:

This one isn’t an install. It’s already built into Claude Code, and most people just aren’t using it. When you finish writing code, type:

• /review: Claude does a structured review of what you just built, hunting for bugs, edge cases, and design issues

• /ultra-review: sends your work to the cloud and spins up a whole team of reviewer agents that each attack from a different angle (logic, security, performance)

Use /review for fast, free feedback on everything. Save /ultra-review for the big stuff: a major change, or anything touching payments or a database.

One more thing

Don’t install all the skills at once.

That’s the fastest way to get overwhelmed and use none of them. Pick one, learn it, build a few things with it, then add the next.

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Summer is coming!

Diana and I prepared a guide on how to use AI on vacation trips. She has more travel experience than I do, but I’ve also added my own experience to complement her ideas.

Imagine this: you’re in Barcelona, it’s 9 pm, every restaurant around you just closed, and you’re starving. You’ve spent the last 30 minutes scrolling Google Maps trying to find one place that’s still open AND serves what you actually want.

No luck.

This used to be me. But in about 20 seconds, AI pointed me to a place 10 minutes away that was still open and served exactly that.

That wasn't the only time AI saved my trip.

After that, I started using AI for almost everything on my last two trips: one across the US West Coast (I’m not from the USA), the other through Portugal. I used ChatGPT, Claude, and Gemini for almost everything: translating to shopkeepers in Cascais, planning a 12-day road trip, finding scenic routes, and even figuring out the location of a beach I saw on Instagram.

Some of these saved me hours. One of them saved an entire day. A few I would never have even thought of without AI.

In this guide, you’ll learn:

• The exact prompt I used to plan a trip in minutes (copy-paste ready)

• How AI helps me save money on my trips

• How to overcome the language barrier using AI

• How to find a place you saw on Instagram (even when nobody says where it is)

• The rules to follow to use AI safely in your trips

• and more!

Here’s how to upgrade your next trip with AI

This guide is exclusive for paid subscribers :)

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I’m fascinated by how this works now. You no longer need a ton of technical knowledge (especially coding) to build tools, automate boring tasks, and more.

I’m not exaggerating. I haven’t written a single line of code when building with Claude.

No programming language needed. Just one language: English.

As Andrej Karpathy said, the hottest new programming language is English. I’ve been literally talking to Claude Cowork to build things. When I say talking, I mean it. I don’t type anymore.

What I do is use voice dictation.

Why? Because you have to give Cowork a lot of detail (just like you’d brief a coworker). If you type all of that, you either skip some details or waste a bunch of time doing it.

So I tested the voice dictation tools out there to find the one that fits this kind of work.

In this guide, we’ll see:

• The built-in voice dictation on your computer (and why it’s usually not enough)

• AI voice dictation tools (with the best use case for each)

• The setup I personally use to talk to Claude

Note: No one is paying me to write this, and I'm not associated with any of these tools. After using them for a while, I actually think most people won't need the fanciest features these tools sell you.

Every week, I publish 3 new guides: some free, some paid.

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Built-in voice dictation (macOS and Windows)

If you've ever tapped the microphone icon on your phone keyboard, you already know what voice dictation is. Same idea, just on your computer.

Here’s how to turn it on:

1. On macOS, press the Globe key twice (set it up in Settings → Keyboard)

2. On Windows 11, press the Windows key + H

A small mic icon pops up. Start talking, and it starts transcribing.

On macOS, it’s basically a real-time transcription. The moment a word leaves your mouth, it shows up in whatever app you’re in.

It’s decent. But it hasn’t caught up with the modern tools out there.

Here are its weak points:

• It transcribes what you say literally (no AI cleaning words like “uh” and “um“)

• The auto-punctuation is basic. You have to say “question mark” to get a “?”

• It has a 30-second timeout that interrupts longer sessions

This might sound like nothing. But these little things change the way you talk. You end up adapting to the tool instead of just speaking naturally.

That's exactly what AI dictation fixes.

They let you talk naturally, like you're talking to a coworker or a friend. The tool adapts to you (not the other way around).

The AI tools below are paid, and I’m going to talk about them based on my own experience.

But here’s my honest advice: try them before you pay. All of them have a free version or demo, and which one fits you really comes down to how you’ll use it.

I mostly use voice dictation for building things in Claude Cowork and Code. For that, I don’t need super polished text or fancy formatting (the AI understands my raw text just fine). So for me, the cheapest option (VoiceInk) in this list already does the job.

VoiceInk: The most affordable, reliable choice (macOS)

VoiceInk is a Mac app that turns your speech into text right where your cursor is, in any app. Everything runs locally on your machine, so your voice never leaves your device (no cloud, nothing sent anywhere).

The best thing? You pay once: $39, no subscription.

The catch: it’s macOS only (and it needs Apple Silicon and macOS 14.4 or later).

As a Mac user, this is enough for me. It does exactly what I need when working with Claude: decent speed, custom vocabulary, and AI cleanup.

The setup that gives me the cleanest text:

1. AI model: gemini-3.1-pro-preview

2. Add tricky words to the dictionary (like “Claude” and “Cowork”)

With that, I get text nearly as clean as what the more expensive tools deliver.

A few honest notes after using it:

• It’s a big step up from the native dictation (better punctuation, fixes your mistakes)

• It cleans up your slips and stops you from repeating words and phrases, kind of like a quick proofread

• It doesn’t auto-format like other tools do (no automatic bullets or numbered lists)

• I’d say it’s around 95% accurate. Once in a while, it drops or adds a random word

For talking to Claude, that’s more than enough. And at $39 once, it’s the easiest one to recommend.

By the way, I discovered VoiceInk thanks to quick review. Thanks, Sabrina!

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Superwhisper: Good option if you're on Windows

I haven't used Superwhisper as much as VoiceInk, but I'm including it for three reasons. It’s good, runs on Windows, and also offers a one-time payment.

The core idea is the same as VoiceInk: it runs locally on your device, drops text wherever you're typing, and keeps your voice off the cloud (as long as you stick to the local models).

What makes it different:

• It works on macOS, Windows, and iOS

• You can tune it a lot (different modes, model choices, custom prompts per app)

• It does more than dictation. It also records meetings and transcribes audio files

Superwhisper has a free tier to test it, a monthly plan ($8/month), and a one-time lifetime license at around $250.

One thing worth knowing before you jump in: all that tuning means a bit more setup. It’s more powerful, but less “install and go” than VoiceInk

If I were on Windows, I’d start with the free version, test it for a few days, and only pay if it clicks (same advice I’d give for any of these).

Wispr Flow: The polished, no-setup option (but pricey)

Wispr Flow is a cloud-based dictation app that turns your speech into polished text in any app. The big difference from VoiceInk and Superwhisper is the heavier AI rewriting.

It makes your text prettier.

Here's what Wispr Flow does that the other two don't (at least by default):

• It generates transcripts faster

• The text comes out more polished: great punctuation, fixes speech mistakes, avoids repetition

• It automatically creates bullets or numerated lists when needed

These features are great. But in my case, I don’t need them that much.

The faster transcript is a few seconds quicker, and I don’t need polished text or bullets when I’m talking to Claude Cowork or Code (the raw text is enough for the AI to understand me).

Wispr Flow is $15/month (for my use, it’s overpriced)

So who is it for? If you want AI-polished text in your emails, documents, and messages with zero setup, and a monthly subscription and cloud processing don’t bother you, Wispr Flow is a great pick.

It’s not for you if you mostly dictate into AI tools (Claude, ChatGPT, etc), because the raw text is already enough there. You’d be paying for features you won’t use.

What’s your favorite tool? Let me know in the comments

P.S. Superwhisper or Wispr Flow aren’t always 100% accurate. Because of the price, I expected them to be way more accurate than VoiceInk, but they occasionally get some words wrong. Again, I recommend trying them yourself.

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A couple of years ago, using AI felt like a superpower. Something that could put you ahead of almost everyone in any room.

Today, it’s starting to feel more like knowing how to use Word or Excel.

Useful, yes. But no longer impressive.

AI is becoming part of the modern work toolkit, not just for tech people.

Writers use it to draft
Marketers test ideas with it
Students use it for assignments
Entrepreneurs use it to move faster

Everyone “uses” AI. Few get anything real out of it.

That’s exactly why just using it won’t protect your job anymore.

The edge now comes from how you use it and what you can actually produce with it: executing faster, communicating better, thinking more clearly, making smarter decisions, etc.

The gap between basic AI use and real advantage looks small. The results aren’t.

One person asks AI for a quick, generic draft.
Another gets it to write in their own voice, matching the company’s style.

One person saves 5 minutes on emails.
Another has systems running while they sleep.

In 2-3 years, “I use AI” won’t mean anything. Everyone will say it.

What will actually matter is what you can do with it that the next person can’t.

This guide gives you exactly that:

• The 3 modes that tell you what to delegate to AI and what to never hand over

• Why a workflow beats a pile of prompts (and the 5 questions I use to build one)

• How to turn AI’s instant answers into real judgment, the skill that doesn’t go obsolete

• The simple formula behind a real professional advantage

The free part showed you the problem. The rest shows you the fix.

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They actually have a context problem.

Claude is smart enough to help with almost anything. But most of the time, the information it needs isn’t in the chat. It’s sitting in your emails, your calendar, your documents, your tickets, your databases, your internal tools.

So the workflow ends up looking like this:

Search for information → Copy it → Paste it into Claude → Wait for a response → Copy the response → Move it somewhere else

It works, but it doesn’t scale.

That’s where Claude connectors come in.

Connectors let Claude talk to apps like Gmail, Notion, Google Drive, and others, so it can pull info from them or take actions on your behalf, instead of you copy-pasting things into the chat.

Skills tell Claude how to work. Connectors give Claude access to where the work actually lives.

In this guide, we’ll see:

• The 10 best Claude connectors

  • Connectors for Docs & Knowledge

  • Connectors for Communication

  • Connectors for Design

  • Connectors for Code & Data

  • Connectors for Business Operations

• How to quickly install a Claude connector

I’ve added a tutorial link to some connectors, in case you’d like to dig deeper.

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The Best Claude Connectors

Claude has dozens of connectors. Here are the best we found

Connectors for Docs & Knowledge

1) Google Drive Connectors

Problem:
Your files are scattered across folders. You know the information is somewhere in Drive, but you still have to search for it, open the file, copy what you need, and paste it into Claude.

Connector:
Google Drive connector lets Claude work directly with your files: search documents, summarize folders, review content, or compare files. Drive is usually where the real context lives, and this connector removes the bridge step between Claude and your documents.

Link: Watch the tutorial

2) Notion

Problem:
Notion is where good ideas go to hide.

You save project notes, content ideas, research, tasks, templates, and half-built plans. Everything is “organized.” But when you actually need something, you end up searching through pages, opening old notes, trying to remember where that one useful idea was.

Connector:
Notion connector connects Claude to your workspace so it can search pages, summarize notes, reorganize information, and turn scattered content into something usable, whether it's internal documentation, a content system, research, or a personal knowledge space.

Link: Watch the tutorial

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Connectors for Communication

3) Gmail Connectors

Problem:
Your inbox knows more about your work than you do.

It has the request, attachment, deadline, follow-up, decision, and probably someone waiting on a reply.

But Claude only sees the email you pasted. So every time you want help, you have to manually reconstruct the conversation first.

Connector:
Gmail connector gives Claude access to your inbox, where every decision, request, and follow-up already lives. It can search threads, summarize conversations, find pending items, and help you prepare replies.

Link: Watch the tutorial

4) Google Calendar

Problem:
Claude can help you plan. But if it doesn’t know your calendar, the planning stays disconnected from what your week actually looks like.

It doesn’t know your meetings, your open blocks, your deadlines, or your focus time.

Connector:
Google Calendar connector gives Claude access to your real schedule so the planning it helps with actually fits your week. It can find open blocks, prepare agendas, detect overloaded days, or help you reorganize tasks.

Link: Watch the tutorial

5) Slack

Problem:
Slack is where a lot of important decisions happen. And it’s also where they get buried fast.

Between threads, replies, channels, and scattered messages, context gets lost.

Then someone asks what was decided, who was responsible, or what the next step was. And you have to search manually.

Connector:
Slack connector gives Claude access to your conversations before the context disappears. It can search threads, summarize channels, identify decisions, find blockers, and turn discussions into next steps.

Connectors for Design

6) Figma

Problem:
Going from Figma to code seems simple until you try to match the design.

The spacing is slightly off.
The components don’t line up.
The layout works, but it doesn’t feel like the original screen.

And then someone from design notices.

Connector:
Figma connector gives Claude access to the design context: frames, layouts, components, variables, and visual structure. It can help generate code or implementation plans from the actual design intent, not from a screenshot.

7) Canva

Problem:
Creating visual content usually means jumping between tools.

You ask Claude for ideas → go to Canva → build the design → go back to Claude to adjust the text → go back to Canva to edit → repeat.

Connector:
Canva connector brings thinking and designing into the same workflow. It can help you go from concept to copy, to layout, to a finished asset you can keep editing. Useful for creators, marketers, and educators building presentations, newsletters, carousels, or branded content.

Link: Watch the tutorial

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Connectors for Code & Data

8) GitHub

Problem:
When you ask Claude for coding help without the repository, the answer stays generic. It doesn’t know your file structure, your open issues, your branches, or how the project is actually set up.

Connector:
GitHub connector lets Claude work directly with your repository so its help goes from generic to specific. It can read files, review issues, check pull requests, look at branches, and help you plan or prepare an implementation.

9) BigQuery

Problem:
Data teams often store important information in data warehouses.

But asking questions about that data usually means going through dashboards, SQL, exports, or an analyst who has access.

Connector:
BigQuery connector lets Claude work directly with your datasets, tables, and queries: review schemas, generate SQL, run queries, and explain results. It removes the friction between the data your team already has and the answers they need.

Link: Watch the tutorial

10) Power BI

Problem:
Dashboards show answers. But they don’t always explain how those answers were built.

A metric comes from a DAX measure.
A visualization depends on relationships between tables.
A filter changes the result.
A report looks clear until someone asks: “Where does this number come from?”

Then you’re no longer looking at a dashboard.
You’re investigating the model.

Connector:
Power BI connector connects Claude with your semantic models: tables, relationships, measures, metadata, and DAX queries. It can help analysts and BI teams audit metrics, explain where a number comes from, and dig into the logic behind a report. The remote server version also lets you query models in plain language while keeping your permissions in place.

Link: Watch the tutorial

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Connectors for Business Operations

11) Stripe

Problem:
Payments, subscriptions, invoices, customers, and refunds all live in Stripe.

When something goes wrong or someone has a billing question, figuring out what happened usually means digging through the dashboard manually.

Connector:
Stripe connector gives Claude direct access to your Stripe data: customers, payment history, invoices, subscriptions, and refunds. When a billing question comes up, Claude can find the context it needs without someone having to pull it manually.

Link: Watch the tutorial

How to install a connector

Here’s how to install connectors on Claude, Claude Cowork, and Claude Code.

Claude Cowork or Web: Customize → Connectors → “+” → Browse connectors → Choose an Connectors

Claude Code:

1. Open the Claude Code terminal and run:

claude mcp add [my-mcp] -- npx -y mcp-package-name

Replace [my-mcp] with the name of the Connectors you want to install.

2. Then verify the installation with

/mcp

If you liked one of the connectors listed, share this guide with others!

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I’ve used ChatGPT to explore new topics (especially with voice mode)
I’ve used NotebookLM to learn from my own materials

Then I started using Claude Cowork and my learning habits changed completely.

My approach today looks completely different. I learn faster, I retain more, and I actually do things while I'm learning instead of just consuming information.

In this guide, you’ll learn:

• How Claude Cowork helps me learn AI faster by doing

• Why voice dictation beats voice mode for learning

• The “learning profile” method that turns Claude into a personal tutor

Heads up: this guide covers my approach to learning using Claude. Some of it transfers to other chatbots, some doesn’t. At the end, I'll share when I reach for ChatGPT and NotebookLM for learning (spoiler: each one wins for a different job)

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