I built and sold a business to HubSpot in just 17 months.

I co-founded Mindstream, an AI newsletter that scaled to 150,000+ subscribers. Today, Mindstream is part of the HubSpot Media Network, where I lead brand strategy for the AI media products. 

Along the way, I’ve grown a 200,000+ following on LinkedIn, sharing real, unfiltered lessons on AI, entrepreneurship, and business growth. The wins and the mistakes.

I believe action beats hesitation. The biggest risk? Never starting.

Adam Biddlecombe

Helping Entrepreneurs Build with AI | Founder, Mindstream (Acquired by HubSpot)

Co-Founder & CEO 

98% think they’re using Claude perfectly.

They open the AI chat box and start prompting.

That’s exactly why they’re leaving 80% of its power unused.

When I first started using Claude, 
I made the same mistake.

I treated it like a chatbot.
Ask question → get answer → repeat.

But now, as I’ve set up Claude properly, 
Everything changed.

It started behaving like a system that actually works with me.
I got:
- Better writing.
- Better research.
- Better coding.
- Better reasoning.

Most people never unlock this because they skip the setup.

That’s why I’ve created this simple breakdown of how you can set up Claude the best way for you.

Here are the key ideas behind a powerful Claude setup:
→ Install Claude properly (desktop unlocks more capabilities)
→ Choose the right model for the job
→ Create a personal voice file Claude can reference
→ Define what you reject so Claude avoids bad habits
→ Give Claude real folders and context to work with
→ Enable features like artifacts and code execution
→ Add job-specific plugins for specialized work
→ Build reusable skills and workflows

When you configure Claude correctly, 
It starts acting like a reliable AI operator.

Do’s:
✅ Set profile preferences so Claude understands your writing style
✅ Give clear instructions and structured prompts
✅ Connect tools and folders for real context
✅ Create repeatable skills for frequent tasks
✅ Test different models based on the problem

Don’ts:
❌ Don’t rely on vague prompts and expect perfect outputs
❌ Don’t leave API keys or secrets in prompts or code
❌ Don’t ignore Projects when organizing complex work
❌ Don’t share sensitive data without checking security settings
❌ Don’t skip testing new Claude features and tools

If you set this up correctly, 
Claude becomes extremely powerful for:
• Writing
• Coding
• Research
• Analysis
• Workflow automation

To see the full setup breakdown, 
Check the infographic below 👇 
It will save you hours of trial and error.

Want to master how to use ChatGPT at work?
Grab my free guide here: https://lnkd.in/eJPyZ-Qe 

Curious to know something:
What do you think about setting up Claude for your workflow?
Comment below 👇

98% think they’re using Claude perfectly.

They open the AI chat box and start prompting.

That’s exactly why they’re leaving 80% of its power unused.

When I first started using Claude, 
I made the same mistake.

I treated it like a chatbot.
Ask question → get answer → repeat.

But now, as I’ve set up Claude properly, 
Everything changed.

It started behaving like a system that actually works with me.
Better writing.
Better research.
Better coding.
Better reasoning.

Most people never unlock this because they skip the setup.

That’s why I’ve created this simple breakdown of how you can set up Claude the best way for you.

Here are the key ideas behind a powerful Claude setup:
→ Install Claude properly (desktop unlocks more capabilities)
→ Choose the right model for the job
→ Create a personal voice file Claude can reference
→ Define what you reject so Claude avoids bad habits
→ Give Claude real folders and context to work with
→ Enable features like artifacts and code execution
→ Add 

If you want to be ahead of everyone in 2026,

You need to build your team of AI employees.

I used to think scaling meant working more hours and doing everything myself.

But now I did something different.
I built an AI team.

Every AI tool acts as an employee.
One for marketing.
One for development.
One for design.
One for research.
One for automation.
And more.

And suddenly, 
Work that took weeks,
Started finishing in hours.

Here’s how people are using AI as employees today:
→ ChatGPT as Chief Strategy Officer
→ Perplexity as Research Assistant
→ Claude as Writer
→ Canva as Designer
→ Zapier as Automation Engineer
→ Replit as Developer
→ HubSpot as Sales Manager
→ Beehiiv as Newsletter Manager
→ Taplio and Tweet Hunter as Ghostwriters
→ Gamma and VEED as Presentation and Video Experts

Most people fail with AI because they treat tools like toys.
High performers treat them like roles.

Do’s:
✅ Do assign one clear role per tool
✅ Do give examples and standards upfront
✅ Do review outputs like a manager
✅ Do combine tools into workflows
✅ Do keep humans in the decision loop

Don’ts:
❎ Don’t expect perfection on first output
❎ Don’t overload one tool with many roles
❎ Don’t skip context and instructions
❎ Don’t blindly trust outputs without review
❎ Don’t chase every new tool without a system

AI doesn’t replace teams.
It replaces friction.

If you want to know all the tools,
Check the infographic below 👇 

It visually maps how each AI fits into a real team.

If you like this and want to get free access to our complete ChatGPT at Work bundle guide, get it here: https://lnkd.in/e6yDSf4F 

What you think about building your own team of AI employees using powerful tools?
Comment below 👇

If you want to be ahead of everyone in 2026,

You need to build your team of AI employees.

I used to think scaling meant working more hours and doing everything myself.

But now I did something different.
I built an AI team.

Every AI tool acts as an employee.
One for marketing.
One for development.
One for design.
One for research.
One for automation.
And more.

And suddenly, 
Work that took weeks,
Started finishing in hours.

Here’s how people are using AI as employees today:
→ ChatGPT as Chief Strategy Officer
→ Perplexity as Research Assistant
→ Claude as Writer
→ Canva as Designer
→ Zapier as Automation Engineer
→ Replit as Developer
→ HubSpot as Sales Manager
→ Beehiiv as Newsletter Manager
→ Taplio and Tweet Hunter as Ghostwriters
→ Gamma and VEED as Presentation and Video Experts

Most people fail with AI because they treat tools like toys.
High performers treat them like roles.

Do’s:
✅ Do assign one clear role per tool
✅ Do give examples and standards upfront
✅ Do review out

Stop using ChatGPT for everything.

The smartest builders use specialised AI tools instead.

Because the right AI tool can save you hours every week.

I’ve noticed something interesting.

Everyone around me kept opening the same tab.

ChatGPT.

For:
Writing.
Research.
Presentations.
Data analysis.
Video ideas.
Coding.

Everything.

But the truth is simple:

ChatGPT is powerful.
But not always the best tool for every task.

The real advantage comes from using specialised AI tools built for specific workflows.

Once I started experimenting with alternatives, 
My productivity accelerated overnight.

Here are some powerful AI tools most people should be using alongside ChatGPT:

Research → SciSpace
Presentations → Gamma
Brainstorming → Claude
Learning → NotebookLM
Data analysis → Rows
Data visualization → Julius
Video generation → Freepik
Image generation → Higgsfield
Marketing copy → Writesonic
Scriptwriting → Sandcastles AI
Content creation → Veed
Development → Replit
Website development → Webflow
Customer support AI → Expertise AI
Deep research → Perplexity Deep Research
Meeting notes → Granola

Each tool is designed for a specific problem.
Which means better outputs and faster workflows.

Instead of forcing one AI to do everything:
✔ Use the best AI for the job
✔ Combine multiple AI tools
✔ Build faster workflows

And the results compound fast.

Do’s:
✔ Use specialized tools for specialized tasks
✔ Combine multiple AI tools in one workflow
✔ Verify important outputs with reliable sources
✔ Use AI to speed up thinking, not replace it
✔ Experiment with different tools regularly

Don’ts:
✖ Do not rely on a single AI tool for every task
✖ Do not skip fact-checking for research outputs
✖ Do not assume AI-generated data is always correct
✖ Do not ignore domain-specific AI tools
✖ Do not replace human judgement with AI

If you like this and want to get free access to our complete ChatGPT at Work bundle guide, get it here: https://lnkd.in/e6yDSf4F 

To see the full breakdown of tools,
Check the infographic below 👇 

And I am curious
Which other AI tool alternative do you think works better than ChatGPT for a specific task?
Drop it in the comments 👇

Stop using ChatGPT for everything.

The smartest builders use specialised AI tools instead.

Because the right AI tool can save you hours every week.

I’ve noticed something interesting.

Everyone around me kept opening the same tab.

ChatGPT.

For:
Writing.
Research.
Presentations.
Data analysis.
Video ideas.
Coding.

Everything.

But the truth is simple:

ChatGPT is powerful.
But not always the best tool for every task.

The real advantage comes from using specialised AI tools built for specific workflows.

Once I started experimenting with alternatives, 
My productivity accelerated overnight.

Here are some powerful AI tools most people should be using alongside ChatGPT:

Research → SciSpace
Presentations → Gamma
Brainstorming → Claude
Learning → NotebookLM
Data analysis → Rows
Data visualization → Julius
Video generation → Freepik
Image generation → Higgsfield
Marketing copy → Writesonic
Scriptwriting → Sandcastles AI
Content creation → Veed
Development → Replit
Website development

Quality of learning always beats quantity of content.

We live in a world where information is infinite.
But attention is not.

For a long time, I thought staying ahead meant consuming more.
More books. 
More newsletters. 
More podcasts. 
More tutorials.

It does not.
It means learning with intention.

Early on, books like Find Your Why shaped how I thought about purpose and direction. 
They gave me language for decisions I could already feel but could not yet explain.

Today, I still return to ideas like that.

Just differently.

I use Shortform.

Not to replace books.
But to understand them better.

Shortform creates book guides that go beyond summaries:

- Deep coverage of core ideas
- Smart analysis and connections across authors
- Interactive exercises that make ideas stick

It feels like your smartest friend walking you through a book.

Three categories I personally spend the most time on:

- Business and entrepreneurship
- Psychology and decision-making
- Communication and negotiation

Here are my favorite books I recommend to learn from:

1. Magic of Thinking Big.
You’ll learn
- Think bigger to unlock higher confidence, ambition, and results.
- Beliefs shape actions, and actions determine success.
- Positive thinking enables better decisions and consistent progress.

2. How to Win Friends and Influence People.
You’ll learn:
- Genuine interest in others builds trust and influence.
- Avoid criticism to maintain strong relationships.
- Make people feel valued to earn cooperation.

3. How to Get Rich
You’ll learn:
- Persistence matters more than talent or luck.
- Wealth comes from execution, not ideas alone.
- Long term success requires resilience and self belief.

The best part is:
On Shortform, I can combine learning from these best books.
To know what I should focus on in the long term.

That is where real leverage comes from.

Shortform also releases new book and article guides every week.
And I get to vote on what gets covered next.

And when I am reading blogs or watching long YouTube videos, 
Their browser extension, Shortform AI, 
Helps me generate high-quality summaries instantly. 
Just in one click.

The result is simple:
- I spend less time skimming.
- More time thinking.
- More time connecting ideas.
- More time building judgement.

Learning compounds when the input is high quality.

If your goal is not just to consume content, 
But to sharpen how you think, 
Shortform is worth exploring.

You do not need more information.
You need better understanding.

Try here: shortform.com/biddlecombe 

Get a free trial and $50 off the annual plan from my link. 

What do you think about learning this new way?
Comment below 👇 

#shortformpartner

Most people think they understand AI.

Until they realise they only recognise buzzwords.

I’ve curated the 40 important AI terms here to fix that gap.

A few months ago, I noticed something strange.

Smart professionals were using AI every day.
But struggling to explain it clearly.

They could say "LLM", "agents", or “hallucinations”.
But not understanding what those actually mean or why they matter.

That is the difference between using AI.
And thinking with AI.

So I broke it down here.

Why this matters more than people realise:
→ Clarity compounds faster than just using AI tools
→ Understanding beats memorising prompts
→ Vocabulary shapes how well you use AI

A few terms most people misuse daily:
• Bias → When training data tilts outputs
• Tokens → How models read and write text
• Inference → Using learned knowledge in real time
• Overfitting → When AI memorizes instead of generalizing
• Hallucinations → Confident answers without factual grounding

Do’s:
✅ Learn concepts before tools
✅ Connect terms to real workflows
✅ Understand limits, not just capabilities
✅ Revisit fundamentals regularly
✅ Explain ideas in your own words

Don’ts:
❎ Chasing tools without understanding basics
❎ Confusing AGI with current LLMs
❎ Ignoring training and data quality
❎ Treating prompts as everything
❎ Assuming AI is always correct

If you want leverage from AI,
You must speak its language first.

To get a quick scan of all 40 AI terms,
Check the infographic below 👇

Learn AI for free: https://lnkd.in/euYZeAdb 

How much time do you invest in learning AI every day?
Comment below 👇

Most people think they understand AI.

Until they realise they only recognise buzzwords.

I’ve curated the 40 important AI terms here to fix that gap.

A few months ago, I noticed something strange.

Smart professionals were using AI every day.
But struggling to explain it clearly.

They could say "LLM", "agents", or “hallucinations”.
But not understanding what those actually mean or why they matter.

That is the difference between using AI.
And thinking with AI.

So I broke it down here.

Why this matters more than people realise:
→ Clarity compounds faster than just using AI tools
→ Understanding beats memorising prompts
→ Vocabulary shapes how well you use AI

A few terms most people misuse daily:
• Bias → When training data tilts outputs
• Tokens → How models read and write text
• Inference → Using learned knowledge in real time
• Overfitting → When AI memorizes instead of generalizing
• Hallucinations → Confident answers without factual grounding

Do’s:
✅ Learn concepts before too

I’ve seen people waste months jumping between random AI hacks.

So I’ve created this one all-in-one AI cheat sheet for you.

To help you master everything important in one go.

Let me be brutally honest.

While you were writing one-line prompts, 
AI pros were using the RTCROS framework to get better results.

While you were guessing prompts, 
They were using chain-of-thought prompting to make AI think step by step.

While you were struggling with manual work, 
They were automating entire workflows with tools you've never even heard of.

Here's what the best AI users know that you don't:

Prompting tips to get 10x better outputs:
→ Zero-Shot gets instant results with no examples
→ Few-Shot guides AI with just 2-3 samples
→ Chain-of-Thought makes AI show its reasoning
→ Persona-Based makes AI adopt expert roles
→ Iterative Refinement keeps making outputs sharper

Tools they're using daily:
✅ Google Nano Banana Pro for stunning visuals in seconds
✅ Cursor for writing code 
✅ Zapier for automating tasks while they sleep
✅ Consensus for research that used to take weeks
✅ Gamma for presentations that look professionally designed

The AI models dominating right now:
→ ChatGPT-5.2 for adaptive multimodal thinking
→ Claude Opus 4.6 for deep reasoning and complex tasks
→ Grok 4.2 for live data and stronger creativity
→ Gemini 3.1 Pro for long-context multi-step workflows
→ Kimi K2 Thinking for hard analytical problems

Some business use cases where AI helps:
→ Digital operations getting fully automated
→ Lead generation running on autopilot
→ Sales cycles being cut in half
→ Marketing campaigns launching without agencies
→ Finance management handled without analysts

The crazy part?
All of this is learnable. 
Right now. 
For free.

The gap between people who invest in learning AI daily and those who don't is growing.

In the next 12 months, 
The professionals who mastered AI prompting, tools, and workflows will be untouchable in their careers.

The ones who didn't will be wondering what happened.
Actually using it every day is where winners are made.

Learn AI for free: https://lnkd.in/euYZeAdb 

How much time per day do you invest in learning AI? 
Be honest, and comment below 👇

I’ve seen people waste months jumping between random AI hacks.

So I’ve created this one all-in-one AI cheat sheet for you.

To help you master everything important in one go.

Let me be brutally honest.

While you were writing one-line prompts, 
AI pros were using the RTCROS framework to get better results.

While you were guessing prompts, 
They were using chain-of-thought prompting to make AI think step by step.


While you were struggling with manual work, 
They were automating entire workflows with tools you've never even heard of.

Here's what the best AI users know that you don't:

Prompting tips to get 10x better outputs:

→ Zero-Shot gets instant results with no examples
→ Few-Shot guides AI with just 2-3 samples
→ Chain-of-Thought makes AI show its reasoning
→ Persona-Based makes AI adopt expert roles
→ Iterative Refinement keeps making outputs sharper

Tools they're using daily:
✅ Google Nano Banana Pro for stunning visuals in seconds
✅ Cursor for writing code 
✅ Zapier for 

Drowning in huge PDFs?

You don’t need more coffee.
You need AI assistance.

Here’s how to use AI for document analysis.

1. Go to ChatGPT
2. Click the ‘+’ icon
3. Add your document
4. Write your prompt
5. Get document analysis done
6. Follow up if you need more insights

Pros:
- Ultra-fast summarisation of long texts.
- Good at extracting key entities, relationships, and topics.
- Can handle multiple formats (text, tables) if converted appropriately.
- Provides a “first-pass” analysis so you don’t start from blank.
- Repeatable and consistent (for the same prompt & input).

Cons:
- May (rarely) hallucinate or invent details that aren’t in the document.
- Doesn’t always catch domain-specific nuance or technical subtleties.
- Performance degrades on very long documents (context window limits).
- No guarantee of source attribution or traceability.
- Biases in training data may skew interpretation.

Do’s
✅ Always give context or purpose before asking for analysis.
✅ Break large docs into smaller chunks for processing.
✅ Ask for both summary and insights/implications.
✅ Request sources or cite-backs for key claims.
✅ Iterate (ask follow-up clarifications) instead of expecting perfection on the first prompt.

Don'ts
❎ Don’t dump an enormous document in one go without context.
❎ Don’t trust the output blindly; always verify critical facts.
❎ Don’t ask vague prompts like “analyse this” without direction.
❎ Don’t ignore structure (tables, figures, sections); treat them explicitly.
❎ Don’t overload with too many goals in one prompt (summary + critique + rewrite + translate all at once).

To get a more detailed explanation,
Check the carousel below 👇 

What do you think of using AI for document analysis?
Comment below 👇 

Learn AI for FREE: https://lnkd.in/eAAacxSK  

♻️ Share this to help your network use AI for document analysis

How To Do Document Analysis For Free With ChatGPT

Entrepreneur & Innovator | Practical AI applications & latest tech trends | Follow to learn how AI can transform your business and save you time.

I create content, build optimization systems, and share insights on AI, high performance, and longevity.

For years, my work has focused on helping people work smarter. But true optimization isn’t just about efficiency; it’s about longevity, energy, and sustaining peak performance.

That’s why I launched longer, a weekly newsletter that simplifies longevity science for people who want to stay sharp, energized, and successful.

What’s happened so far:
→ 22M+ social media impressions in the last year
→ 60K+ people follow my insights on AI, longevity, and high performance
→ A growing community applying these ideas to work, health, and life

I’ve also invested in companies in emerging markets, always looking at what’s next in innovation and long-term impact.

My focus is on systems that improve how we work, think, and live, not just today, but for the long run.

Anna Poplevina

In business, doing the “hard work” is the bare minimum.
Smart work is what separates millionaires from billionaires.

Smart work means building systems that work while you sleep.
It means automating the repetitive so you can focus on the irreplaceable.
It means using technology to 10x your output without burning out.

Most founders get this when it comes to marketing or sales.
They automate emails. They use CRMs. They build funnels.

But there's one area where they're still stuck in hard work mode:
Their finances.

They're either:
→ Paying $200K+ for a CFO
→ Drowning in spreadsheets themselves
→ Flying blind until it's too late

Even with a CFO, you're getting yesterday's news.
While tomorrow's problems are already forming.

I discovered something that completely flips this.

An AI CFO technology that works nothing like ChatGPT or Claude.

You can't just prompt your way to financial insight….

❌ LLMs = Wait for you to ask
✅ AI CFO = Flags issues before you do

❌ LLMs = Context from what you upload  
✅ AI CFO = Connected to your accounts with real-time data

❌ LLMs = Reactive tools you remember to check
✅ AI CFO = Proactive alerts pushed to Slack and email

That's the difference: LLMs wait for you to ask. AI CFOs flag issues before you do.

One founder told me: "It pointed out things I would normally discover weeks later."

Reality check:
Most startups can't afford a $200K CFO.
But missing one cash flow warning costs way more.

2026 isn't about writing better prompts.
It's about choosing the right AI for your business.

Want to know how you can save $200K on CFO costs? 
Link in the comments.

----

👋 Click 3 dots top right corner to save for later
♻️ Repost this to save someone hours.

22M+ views | Creator, Entrepreneur & Biohacker | Follow for AI strategies and performance systems to build without burnout | Building longer. | Want to collab? 👉 collabs@annapoplevina.com

AI can now do finances like a $200K CFO hire.
But generic LLMs can’t.

I tested both.

You can't just use ChatGPT or Claude.

❌ They don't connect to your bank account.
❌ They don't monitor your business in real-time.
❌ They don't email you alerts. Or ping you on Slack.

AI CFOs are built differently.

✅ Direct connection to your accounting and bank.
✅ Monitoring 24/7 in the background.
✅ Proactive alerts pushed to Slack or email before problems hit.

You don’t have to upload spreadsheets.
It’s already connected to your bank and accounting.

That’s when I realized something:

❌ LLMs = Wait for you to ask
✅ AI CFO = Flags issues before you do

❌ LLMs = Context from a prompt
✅ AI CFO = Context from your actual numbers

❌ LLMs = Tools you open
✅ AI CFO = Built into your financial system

Recently, a founder who set one up said:

“The Sunday night cash flow anxiety is just gone.”

That’s the difference.

Most startups can’t afford a $200K CFO.
But missing a cash flow warning can cost far more.

2026 isn’t about writing better prompts.
It’s about choosing the right AI for your business.

Curious what I used for my AI CFO? 
I’ll post the link in the comments.

taplio

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The Battle

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