Then I opened Hyperagent and gave it 90 minutes.

By the end I had a complete YouTube operating system - a workflow my future self can run on autopilot, plus a custom AI “skill” that does the heavy lifting for me every Monday morning. I also burned $41 in API credits getting there.

But hold that thought. Hyperagent is awarding $1000 in free credits to early users and I’ve been given 20 of them to hand out. Skip to the bottom if you want one.

What Hyperagent Actually Is

For anyone who hasn’t seen it: Hyperagent is basically a chat interface where the AI doesn’t just answer you. It can browse the web, read pages, write files, build documents, save reusable “skills” you can invoke later, generate images, and chain all of that together inside one conversation. Think Claude or ChatGPT, but the AI actually does the work instead of just describing how you’d do it.

I’d been hearing about it for a few weeks and finally bit. Here’s what I built.

What I Actually Did

I started by asking Hyperagent to audit a few high-performing AI YouTubers in my niche. Not me - other channels. The ones consistently pulling 100K+ views. I told it to decode their title formulas, thumbnail patterns, video lengths, posting cadence, and what was actually driving their views.

It opened a browser, scraped their channels, pulled view counts, identified five recurring title formulas, mapped their content into pillars, and gave me a side-by-side comparison with my own channel. It did take 30 minutes but was well worth it.

Then I asked it to design a workflow that leverages the identified patterns without copying them. It came back with a positioning rewrite, a three-pillar content mix, a posting cadence I could actually sustain, and a “leverage map” showing what to borrow versus what to differentiate on.

So far, useful. But here’s where it got interesting.

I asked Hyperagent to turn the whole thing into a reusable skill - the kind that lives in my account and runs whenever I invoke it. It built me a two-stage system:

Stage 1 is research. I say “what should I make next?” and it scrapes my reference channels, checks the internet for new AI-related feature releases, scores topic candidates on a five-criterion rubric, and gives me a ranked shortlist of five video ideas across my three pillars. Then it stops and waits for me to pick.

Stage 2 is production. I pick a topic, say “build the package for it,” and it generates a full production doc … five title variants, a 2,000+ word script in my voice, a thumbnail brief specific enough my designer doesn’t need follow-ups, twenty SEO tags, the description, chapter timestamps, a pinned comment, and end-screen instructions. One doc, hand it to my editor.

I tested it the same afternoon. Running Stage 1, it surfaced a Claude feature that dropped six days ago that no major YouTuber had covered yet. Pure whitespace. I’m filming the video this week.

While we were at it, Hyperagent also wrote me a tool-agnostic editor brief template I’m sending my editor on day one, a thumbnail A/B testing protocol, and a feedback loop where I paste my video performance back into the skill every week so it gets sharper over time. It also generated a polished webpage version of the entire system that I can bookmark and re-read.

The whole thing … audit, leverage map, skill, editor brief, bonus pieces, the wrap-up webpag, landed in about 90 minutes of back-and-forth.

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What’s Quietly Profound About This

Six months ago this kind of workflow would have meant hiring a YouTube strategist, then a content writer, then a trends analyst. Or carving out a week to figure it all out yourself. Or following yet another guru’s $999 course.

What I actually did was open a chat window, describe what I was stuck on, and let the AI do the work. I made the creative calls. The audit was real research, not generic advice. The skill is mine … it lives in my account, runs forever, and gets sharper the more I feed it performance data.

The shift isn’t “AI helps me think.” It’s “AI builds the thing.” And the thing keeps working after the chat ends.

The Honest Trade-Offs

I owe you the trade-offs because Hyperagent is not free.

The 90-minute session burned $41 in API credits. That sounds like a lot for one conversation, and it is … but Hyperagent is doing real work. It opened browsers. It scraped multiple sites, even watched videos on my behalf! It generated artifacts. It updated a working doc throughout. The cost reflects actual compute, not chat tokens.

For comparison, I’d have paid more than $41 for an hour with a YouTube strategist who’d have given me less than what I got. But still … go in with eyes open. Big builds chew through credits. If you’re just dabbling, you’ll burn a starter balance faster than you think.

The other trade-off: it’s powerful enough that you can absolutely waste credits asking it dumb questions. Use it for the heavy lifts … researching, building, generating. Don’t use it for things you’d ask Claude or ChatGPT about for free.

The Codes

Now the good news.

Hyperagent is awarding $1000 in free credits to early users to actually try building stuff with it and they’ve given me 20 codes to share with subscribers of this newsletter. Each one is enough to comfortably build something real.

https://hyperagent.com/refer/KZGYSWFT

First-come-first-served until they’re gone.

I’m genuinely curious what you’ll build. The point of these tools isn’t replacing the work … it’s expanding what one person can ship in an afternoon.

Cheers, Jagger

If this made you think of someone who’s been “going to fix” their workflow for the past year, send it to them.

It’s on my Mac. At home.

I didn't have it on me - which meant paying $3 to print a page at the counter anyway. But first, I had to actually get the document.

So I pulled out my phone, opened Claude’s Dispatch tab, and typed: “Find my visa docs as I need a few of them.”

Thirty seconds later, Claude had searched my Documents, Desktop, and Downloads, found a Visa folder sitting right on my Desktop, and listed everything in it. Passport scan. VEVO check. IMMI bridging visa grant notification. Invitation Letter. The full set.

I typed: “Email me my invitation letter.”

Claude used Mac Mail, attached the PDF from my Desktop, and sent it to my Gmail. While I was standing there. While the officer was still processing my other documents.

Here’s what Dispatch actually is, because I think a lot of people are sleeping on it.

It’s a feature in Claude that lets you control your Mac from your phone - like computer use, but for your actual desktop. You leave a session running at home, and from anywhere in the world, you can ask Claude to navigate your machine, find files, open apps, click things, send emails. Real actions on your real computer.

It’s not screen sharing where you’re squinting at a tiny mirror of your desktop. You just... describe what you need, and Claude does it.

There’s something quietly profound about this that I keep thinking about.

AI has been good at generating things - text, images, ideas. That’s impressive but still kind of abstract. What Dispatch represents is AI that retrieves things. Things that already exist in your actual life. Your files. Your emails. Your calendar. Your applications.

The gap between “I need X” and “I have X” collapsing in real time … that’s where AI gets genuinely useful for normal people doing normal things.

Not writing blog posts. Not summarising PDFs. But finding the document you need while you’re sweating in a government office.

If you want to try this yourself, here’s the setup:

1. Download Claude for desktop (Mac for now)

2. Enable Dispatch in settings - it keeps a persistent session running in the background

3. Download Claude on your phone and navigate to the Dispatch tab

4. Leave your Mac on when you leave the house

That’s it. You now have an AI that can reach into your home computer from wherever you are.

Start with something low-stakes - ask it to find a file, check something in a browser, pull up an email. Get a feel for it. Then you’ll naturally start remembering it exists in the moments that actually matter.

Like a visa office on a Tuesday.

If this made you think of someone who’s always scrambling to find documents at the worst possible time, send it to them.

Cheers, Jagger

Not gently. Not diplomatically. Bluntly.

Jensen Huang - the man whose chips are inside basically every AI system on earth - was asked a simple question: if AI is supposed to make everyone more productive, why are companies laying people off?

His answer: “Because you’re out of imagination.”

Let’s call it what it really is - the CEO of Nvidia just called out his own biggest customers on national television. Meta. Amazon. Microsoft. Companies spending tens of billions on his hardware, while simultaneously cutting tens of thousands of jobs and citing AI as the reason.

Meta is reportedly cutting 15,000 people — 20% of its workforce — while doubling its AI budget to $135 billion this year. Amazon eliminated 16,000 corporate roles in January and explicitly named AI as the efficiency engine behind the decision. Microsoft cut more than 15,000 positions across 2025 while committing $80 billion to AI infrastructure.

These are the companies Jensen is talking about. His biggest customers. And he went on TV and said they lack imagination.

That’s a gutsy thing to say. It’s also not the whole story.

Here’s the thing nobody’s talking about: Jensen’s framing is inspiring and probably true at the company level. It’s a much harder sell at the human level.

His argument goes like this - when a company gains new capability, the right move is to ask what you can now build that was previously impossible. What new market can you enter? What product can now exist? The ambitious answer to “we can do more with less” is do more - not employ less.

And he’s right that some companies will do exactly that. The ones that use AI to expand instead of contract will probably eat the ones that don’t.

But Jensen’s argument assumes that the workers being cut will land somewhere in this expanding vision. That the 16,000 people who left Amazon in January are going to become AI-era architects of something new. That the answer to “I just lost my job to automation” is “don’t worry, a more imaginative company will find you.”

That’s not imagination. That’s optimism wearing imagination’s clothes.

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Dario Amodei - the CEO of Anthropic, the company that built Claude - has been more direct about what’s actually coming. He’s warned that AI could eliminate 50% of entry-level white-collar jobs within five years. Not because companies lack imagination. Because the economics are straightforward.

The Dallas Federal Reserve already has data showing workers aged 20–30 in AI-exposed roles have seen unemployment rise nearly three percentage points since early 2025. That’s a real shift, in a short timeframe, concentrated in exactly the people entering careers right now.

So where does that leave you?

If you’re a company leader, Jensen’s framing is the one worth holding onto. The organisations that use AI to shrink will hit an efficiency ceiling fast. The ones that use it to expand - new products, new markets, shorter cycles - will compound in ways that are genuinely hard to replicate. The technology is identical. The difference is what you’re trying to build.

If you’re an individual, the honest answer is messier. Waiting to be redeployed into an imaginative company’s expanded vision is not a strategy. The uncomfortable truth is that the burden of navigating this transition is falling on people, not organisations.

That’s not a reason to panic. But it is a reason to move.

If you’re not already building fluency with the tools that are reshaping your field, start this week. Not a course. Not a YouTube rabbit hole. Pick one task you do regularly at work and spend an hour trying to do it with AI. See what happens. That friction - the moments where it works and the moments where it doesn’t - is more valuable than any explainer article.

The people who come out of this period well won’t be the ones who had the most optimistic boss. They’ll be the ones who stopped waiting to be told what to do with the new tools and just started doing things.

Jensen’s right that imagination is the variable. He just forgot to mention whose imagination matters most right now.

It’s yours.

If you found this useful, share it with someone who’s been asking whether their job is safe.

Cheers, Jagger

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The moment I saw it, I thought: that should be Precious.

Precious is my 6-year-old Samoyed. She’s an enormous white cloud of fur who’s cute as anything, full of sass, quite clumsy, and deeply unimpressed by most things... I wanted her on my dock. I wanted to click her and chat with her. I had no idea how to build that. I did it anyway.

Here’s exactly how, so you can build yours too.

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Step 1: Get Inspired (and Steal Shamelessly)

Go look at Lil Agents on GitHub: https://github.com/ryanstephen/lil-agents

It’s the reference for this whole project. Ryan built it with animated characters that walk along your dock and open a streaming chat window when clicked. The code handles all the hard macOS stuff - window positioning, transparent layers, dock integration. Rather than starting from scratch, I used it as the blueprint and built something inspired by the approach. Thank you Ryan!

You don’t need to fork it or understand Swift deeply. You just need to understand what it does so you can describe what you want to an AI and have it build your version.

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Step 2: Create Your Pixel Art with Nano Banana Pro

This is where most people would get stuck. Character art is hard. Consistency across multiple poses is even harder.

I tried a few AI image tools and Nano Banana Pro was the clear winner - specifically because of character consistency. Here’s the problem with most AI image generators: generate your dog in a walking pose, then generate her sitting, and you’ll get two different dogs. Different shading, different proportions, different face. It looks terrible in a sprite animation.

Nano Banana Pro locks onto the character. Once it has Precious - her fluffy ears, her curl of a tail, her general vibe - it keeps her consistent across every frame you generate. That’s the thing that makes this actually work.

Here’s what I generated and roughly what to prompt:

Idle / Standing:

“Pixel art Samoyed dog, fluffy white fur, black outline, side view facing right, standing idle pose, transparent background, 64x64 sprite, cute chibi style”

Walk Cycle (4 frames):

“Pixel art Samoyed dog sprite sheet, 4-frame walk cycle, 2x2 grid, side view facing right, fluffy white, transparent background, 64x64 per frame, same character as reference image”

Sitting:

“Pixel art Samoyed dog sitting, same character as reference, fluffy white, side view, transparent background, 64x64”

Sleeping:

“Pixel art Samoyed dog lying down, eyes closed, curled up, fluffy white, same character, transparent background”

Happy (tail wagging):

“Pixel art Samoyed dog, excited expression, tail raised, same character, fluffy white, transparent background”

Generate your idle pose first. Then use it as a reference image in every subsequent generation - that’s how you get consistency. Save each one as a PNG with transparency. Name them: idle.png, walk_sheet.png, sit.png, sleep.png, happy.png.

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Step 3: Build the App with Claude Code

Open Claude Code and paste this prompt exactly:

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“I want to build a macOS desktop companion app in Swift using Swift Package Manager (no Xcode needed). The app should:

- Display a transparent floating window just above the dock

- Show an animated pixel art sprite of my dog that walks back and forth

- Have 5 sprite states: walking, idle, sitting, sleeping, and happy

- The walk_sheet.png is a 2x2 grid of 4 frames - split it into individual frames in code

- Behaviour based on activity: walk/idle when recently active, sit after 20 seconds idle, sleep after 60 seconds idle

- When I click the dog, open a dark-themed chat window

- While waiting for an API response, show the sitting sprite

- When a response arrives, show the happy sprite for 2 seconds

- Connect to the Anthropic API using streaming (claude-sonnet-4-6 model)

- Ask for my API key on first launch and store it in UserDefaults

- Show a small menu bar icon with options to update the API key and quit

- Images will be in the app bundle’s Resources folder

The system prompt for the AI should be: ‘You are [your dog’s name], a friendly [breed] who lives on the user’s Mac. Be warm, helpful, and playful.’

Start with Package.swift and work through each Swift file. Build a build.sh script that compiles with swift build -c release and packages the result into a .app bundle.”

---

It will ask clarifying questions. Answer them. Run the code it gives you. When something breaks, paste the error back in and say “fix this.” Iterate. This is the loop.

It took me an afternoon. Genuinely.

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Step 4: Drop In Your Sprites and Launch

Once your app compiles and build.sh runs successfully:

1. Right-click your .app → Show Package Contents → Contents → Resources

2. Drop in your 5 PNG files with the exact names above

3. Launch the app

4. Enter your Anthropic API key when prompted (get one at console.anthropic.com)

5. Your dog appears above the dock (mine is hidden)

That’s it.

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What This Actually Shows

There’s a bigger point here that goes beyond building a desktop pet.

Six months ago this project required knowing Swift, AppKit, sprite animation, API integration, and macOS window management. It would’ve taken a developer a few days. The pixel art would’ve taken an artist.

Now it took one afternoon and two tools: Nano Banana Pro for the art, Claude Code for the build. I described what I wanted. I iterated when things broke. I made design decisions - Precious’s animations, her personality in the system prompt, what triggers each state. The creative and design judgment was mine. The execution was AI.

Precious is on my dock right now. She just sat down, which means I’ve been ignoring her for twenty seconds. She’ll go to sleep soon if I don’t click her.

I find this unreasonably delightful.

If you found this useful, share it with someone who’s been waiting until they “know enough to build something.”

Cheers, Jagger

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The trick? A tool called Ollama. It lets you run open-source language models on your own computer, and Claude Code can plug straight into it. No API key. No monthly bill. No cloud.

Here’s how to set it up in about 10 minutes.

What’s Actually Happening Here

Claude Code is the tool - it’s the interface you type into, the thing that writes and edits code for you. Normally it connects to Anthropic’s servers in the cloud, which costs money.

Ollama is like a mini AI server that runs on your laptop. It downloads an open-source model and serves it up locally. So instead of Claude Code talking to the cloud, it talks to Ollama sitting right there on your machine.

Same experience. Zero cost. The trade-off? Your computer does the heavy lifting, so you’ll want decent RAM. And the local models aren’t quite as sharp as the cloud versions. But for learning, experimenting, and building stuff? More than good enough.

Step 1: Install Ollama

Head to the Ollama website and download it. Works on both Windows and Mac.

Step 2: Download a Model

Open up your terminal (PowerShell on Windows, Terminal on Mac) and run:

ollama run gpt-oss:(size)

Replace (size) with whichever variant suits your machine. The general rule: bigger model = smarter responses = needs more RAM. If you’ve got 16GB of RAM, you’ll be fine with the mid-sized options. If you’ve got 32GB+, go large.

You can browse all available models on the Ollama website. Pick one with a large context length - that’s what lets it handle bigger codebases without losing the plot halfway through.

Step 3: Install Claude Code

On Mac:

curl -fsSL https://claude.ai/install.sh | sh

On Windows (PowerShell):

https://claude.ai/install.ps1 | iex

Windows users - if the claude command doesn’t work after install, you might need to add it to your system path. Go to System Properties → Environment Variables → System Variables → Path → New → then paste your install directory (usually C:\Users\<your-username>\.local\bin).

After that, typing claude in your terminal should fire it up.

Step 4: Connect Claude Code to Your Local Model

This is where the magic happens. You’re telling Claude Code to stop looking at the cloud and start talking to your local Ollama instead.

The easiest way:

ollama launch claude

Then select the model you downloaded earlier. Done.

If that doesn’t work for whatever reason, you can do it manually with environment variables:

On Mac:

export ANTHROPIC_AUTH_TOKEN=ollama
export ANTHROPIC_BASE_URL=http://localhost:11434
claude --model gpt-oss:(size)

On Windows (PowerShell):

$env:ANTHROPIC_AUTH_TOKEN="ollama"
$env:ANTHROPIC_BASE_URL="http://localhost:11434"
claude --model gpt-oss:(size)

First time you run it, Claude Code will ask you to confirm that your project files are trustworthy. Say yes and you’re in.

That’s It. You’re Running Claude Code for Free.

No subscription. No API credits ticking down. No anxiety about how many prompts you’ve got left this month. Just you, your machine, and a local AI ready to build stuff.

Want to Test It? Build a Pomodoro Timer.

Seriously. Once you’re set up, paste this into Claude Code and watch what happens:

“Build a classic Snake game in Python with Pygame. Grid-based movement, arrow key controls, food spawning, score counter, increasing speed as the snake grows, wall and self-collision detection, game over screen with final score. Single file, clean code.”

If it spits out a working Pomodoro Timer, your setup is solid.

The Honest Trade-Offs

I’m not going to pretend this is identical to the paid cloud experience. It’s not. Local models are generally less capable than the latest cloud models. They can be slower depending on your hardware. And if you’re working on a massive codebase, you might hit context limits faster.

But for learning how AI-assisted coding works, for building side projects, for experimenting without worrying about cost - this is genuinely brilliant. It’s also a great way to figure out if the paid version is worth it for you before committing.

And there’s something satisfying about the whole thing running on your machine. No internet required. No data leaving your laptop. Just pure local AI vibes.

If you found this useful, send it to someone who keeps saying AI tools are too expensive to try.

That’s not my prediction. That’s the logical conclusion of what’s already happening with personal AI agents and it’s being driven by the same kind of shift that killed Blockbuster, turned newspapers into websites, and made your local travel agent obsolete.

Let me explain.

The MyFitnessPal Problem

Here’s a question: why do you need a calorie tracking app when your AI agent already knows where you are?

Think about it. Your personal agent knows your schedule. It knows you just walked into a restaurant. It knows you’ve been trying to eat cleaner this month. It knows you had a heavy meal last night. It knows your weight goals.

So when you sit down at that restaurant, your agent can already suggest what to order - without you ever opening an app, scanning a barcode, or manually logging anything.

Peter Steinberger, the creator of OpenClaw (the open-source AI agent that just hit 180,000+ GitHub stars), put it bluntly in his recent conversation with Lex Fridman: “Why do you need MyFitnessPal when the agent already knows where I am?”

He’s right. And MyFitnessPal is just the tip of the iceberg.

The Pattern Is Everywhere

Once you see it, you can’t unsee it.

Your sleep tracker app? Your agent already knows how you slept — it can pull data from your wearable, adjust your gym workout based on your recovery, and text your training partner that you’re doing a lighter session today. No app required.

Your smart home app? Steinberger described how his agent controls his cameras through their API directly. No need to open the Sonos app when your agent can talk to the speakers. No need for the Ring app when your agent already has access.

Your calendar app? “I don’t wanna open a calendar app,” Steinberger said. “I just wanna tell my agent, ‘Hey, remind me about this dinner tomorrow night,’ and maybe invite two of my friends and send a WhatsApp message to them.” One sentence replaces three apps.

Your Uber Eats app? Your agent can figure out how to use your phone, open the browser, navigate the site, and place the order. Or call an API directly. Or find a better deal elsewhere. The app becomes invisible — just a slow API your agent calls on your behalf.

Every App Is Now Just a Slow API

This is the key insight that most people are missing.

It doesn’t matter whether a company wants to be agent-friendly or not. If your AI agent can access something through a browser, it’s already an API. A slow one, sure. But a working one.

Steinberger watched his agent happily click the “I’m not a robot” button on websites. Cloudflare and others are trying to block bots, but the reality is straightforward: if a human can access it through a browser, an agent can too. It’s just a matter of speed.

Companies that get ahead of this — that build proper APIs and make themselves easy for agents to work with — will thrive. They become the preferred, fast path.

Companies that fight it? They just become the slow path. And eventually, users stop caring which path their agent takes.

Who Dies, Who Survives

Here’s the split as I see it:

Dead apps walking: Single-purpose utility apps that exist to display or capture simple data. Calorie trackers, habit trackers, basic to-do lists, weather apps, simple finance dashboards, most smart home controllers. Anything where the core value is “show me this data” or “let me input this data.” An agent does both better because it has context these apps never will.

Apps that transform: Companies that rapidly pivot to being agent-facing services. Uber Eats doesn’t die — it becomes the delivery API your agent calls. Spotify doesn’t die — it becomes the music service your agent manages. The app layer disappears, but the underlying service survives. The companies that build clean, agent-friendly interfaces fastest will win this transition.

Apps that survive: Anything where the core value is the experience itself, not the utility. Games. Creative tools where you want to be hands-on. Social platforms (though even these will change dramatically). Anything where removing the human interaction removes the point.

What This Means for You

If you’re a builder or developer: start thinking about how your product works when no human ever opens your UI. What’s your API story? What’s your agent story? Because the companies that answer those questions first will eat the lunch of those that don’t.

If you’re a user: this is genuinely exciting. The subscription fatigue, the app clutter, the constant context-switching between 15 different tools to get one thing done — that era is ending. Your agent handles the mess. You just say what you need.

If you’re an investor: pay very close attention to which companies are building for the agent-first world versus which are still building for the app-store world. That distinction is about to matter a lot.

The age of the app isn’t ending overnight. But the age of the app as the primary interface between you and digital services is absolutely ending. Your agent is becoming your operating system.

And that operating system doesn’t need 80 apps. It needs APIs, data, and permission to act on your behalf.

That’s it for this week. If you found this useful, share it with someone who’s still manually logging their calories.

Formula 1 coming back to Melbourne always hits different.

It’s the first race of the season.
Fresh cars.
New regulations.
Everyone saying they’ve “nailed it” until Friday practice proves otherwise.

This year feels bigger than most.

Regulation changes don’t just reshuffle the grid - they expose which teams actually understand the systems underneath. And that’s why I’m more excited than usual.

Because Formula 1 isn’t just resetting aerodynamics or car concepts.

It’s doubling down on AI.

Formula 1 Is No Longer Won on Track Alone

We still like to believe races are decided by bravery under braking or raw driver talent.

That’s part of it. But it’s no longer the whole story.

Modern Formula 1 is about decision systems.

• Which setup to bring

• How aggressively to run the tyres

• When to pit … and when not to

• How to react when the race doesn’t follow the plan

Those decisions are now shaped - heavily - by AI-driven simulation and modelling.

Teams run thousands of simulations before a race weekend even starts. Not just for strategy, but for car setup, performance trade-offs, and failure scenarios. AI helps narrow an almost infinite set of possibilities down to a handful worth trying.

This isn’t “AI replaces engineers”.

It’s AI deciding where engineers should focus.

AI Simulation in Formula 1: What Teams Actually Use It For

When people hear “AI in F1,” they often think it’s abstract.

It’s not.

AI is already embedded in:

• Simulation workflows

• Strategy modelling

• Engineering decision loops

• Reliability and performance trade-offs

The goal isn’t perfection. It’s speed.

Speed of learning.
Speed of narrowing options.
Speed of making the least-worst decision under pressure.

What I Saw First-Hand at the Melbourne Grand Prix

Last year around the Australian Grand Prix, I attended a Dell x McLaren session where Dan Keyworth, Head of Business Technology at McLaren Racing, walked through how McLaren uses AI and data across the team.

What stood out wasn’t the tech buzzwords.

It was how operational everything was.

AI wasn’t a future roadmap item.
It was already part of how the team functioned day to day.

McLaren talked about AI the same way teams talk about hydraulics or telemetry.

Not optional.
Just part of the machine.

And you could see it working.

Results Don’t Lie

Fast forward to the season results:

• McLaren won the Constructors’ Championship

• Lando Norris won the Drivers’ Championship
  (though I’d be lying if I said I wasn’t rooting for hometown boy Oscar Piastri)

That doesn’t happen by accident.

AI Is No Longer “Quietly” Reshaping Formula 1

People like to say AI is quietly reshaping Formula 1.

I don’t think that’s true anymore.

It’s not quiet.
It’s central.

The difference is most fans only see the output … not the system.

They see:

• A perfect undercut

• A bold tyre call

• A strategy that “just worked”

What they don’t see:

• The models that ran overnight

• The simulations that killed bad options before the race began

• The decision systems ranking scenarios in real time while cars fly past at 300km/h

AI has become the backbone of how F1 teams think.

To prove this point, just a few days ago, Mercedes announced a partnership with Microsoft, where they will aim to turn data into insights, smart decisions and ultimately performance on the track. AI plays a huge role in this.

Why Regulation Changes Make AI Even More Important

Every time Formula 1 introduces regulation changes, uncertainty spikes.

That’s when AI matters most.

When nobody knows what “optimal” looks like yet, the teams with the best simulation, data infrastructure, and decision-making loops adapt faster.

They:

• Test more ideas digitally

• Fail cheaper

• Converge on better solutions sooner

With Melbourne as the first race, we’re about to see who actually understands their systems - and who’s still guessing.

Why Most Fans Still Underestimate AI in Formula 1

Ask the average fan about AI in F1 and you’ll hear:

• TV graphics

• Strategy predictions

• Cool dashboards

That’s the shallow layer.

The real impact is upstream:

• Design decisions made months earlier

• Strategy frameworks built long before lights out

• Operational resilience when things go wrong

Formula 1 teams now operate more like high-performance software organisations than traditional racing teams.

AI is the connective tissue.

Why This Story Matters (and Why I’m Telling It)

I’m a massive Formula 1 fan.
I’m also deep in the AI space.

What excites me about Formula 1 right now isn’t just the racing - it’s how clearly it shows where AI actually adds value.

As the cars line up in Melbourne on 8 March 2026, with new regulations and fresh uncertainty, we’re not just watching a race.

We’re watching the output of thousands of AI-driven decisions made long before the engines fire.

Most people have no idea how much of the sport now runs on that invisible layer.

That’s what makes this season fascinating.

I can’t wait.

—
Jagger

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I can’t use Sora yet. It’s only live in the US and Canada.
But over the past few days, I’ve watched enough clips to see what’s coming and it’s wild.

Not because the tech is magic (it’s not).
But because it might be the first AI product that feels social instead of smart.

The first video that stopped me scrolling

I saw a short clip of someone walking through a rainy street, talking casually to the camera. The reflections on the pavement looked real. The person’s face, the way the jacket moved … all believable.

It wasn’t filmed. It was generated from text.

Then I noticed something else: every comment was someone tagging a friend saying, “Make one of you doing this.”

That’s when it clicked … Sora isn’t just an AI video tool. It’s an interaction machine.

From “tools” to “toys”

Most AI products are built for utility … they help you write, code, summarise, automate.
Useful, sure, but soulless.

Sora is different. It’s designed for play.
It’s closer to TikTok than ChatGPT. You don’t go there to get something done. You go to see what happens.

That’s a shift that matters.

AI so far has been single-player: you prompt, it outputs.
Sora looks like the first multi-player experience.

You can include people you know, remix their clips, or make versions of shared jokes. It’s not about creating, it’s about participating.

That’s the ingredient that made TikTok take off … not the algorithm, but the feeling that everyone’s in on the same bit.

Watching from the sidelines

Being in Australia, I can’t try it yet.
But it’s interesting to see what people are doing when the tech is still limited.

They’re not pushing for realism. They’re testing boundaries.
“How far can I push this prompt?”
“Can I make a scene that feels slightly wrong?”
“Can I make something that looks like my memory of an event, not the event itself?”

It’s playful in a way AI hasn’t been in a long time.

That’s what scares me (and excites me). Once it lands here, everyone’s going to flood their feeds with clips. Not because they want views, but because it’s frictionless fun.

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The hidden shift

If you zoom out, Sora isn’t about deepfakes or content creation. It’s about attention.

TikTok owns short-form human attention.
Sora could own synthetic attention … infinite, personalised, self-referential media.

That’s not a claim that it’ll kill TikTok overnight. But the pattern is familiar:

• Lower barrier to creation.

• Built-in remix culture.

• Algorithmic distribution that rewards experimentation.

The more people use it, the better it gets.
And the better it gets, the more we’ll start trusting synthetic media as normal.

My take

This isn’t “the next big thing.” It’s the next inevitable thing.

AI tools started as productivity software.
Now they’re becoming culture engines.

And when the culture moves, the money follows.

For now, I’m happy to sit back and watch the chaos unfold from here in Melbourne.
But I don’t think it’s hyperbole to say: once this rolls out globally, every creator, brand, and even meme account will have to rethink what “video” means.

Because when everyone can generate reality on demand, the only thing left that’s real … is taste.

Subscribe if you haven’t.

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- Jagger

This is the go-to newsletter and community for no-code AI tools, news and productivity insights.

Did you know that the most in-demand tech skill of 2025 isn’t Python or JavaScript-it’s Vibe Coding.

Let that sink in.

The biggest opportunities in tech aren’t going to programmers anymore-they’re going to Vibe Coders.

The best thing... this course works for Absolute Beginners!

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If we have not met, I’m Jagger-an AI Product Manager, AI agency founder, and published in AI space-and I’ve worked hard to create the easiest path to building tools using AI without code (=Vibe Coding).

Why This Course is Different

Most AI courses teach theory. We give you the exact tools & skills to:

• Launch AI products (like his agency’s client apps)

• Automate workflows (tested in real businesses)

• Solve problems fast (no coding, no guesswork)

P.S. In 2025, there will be two types of people: Those who use AI apps, and those who get paid to build them. Which side will you be on?

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Jagger

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TL;DR: Traditional SEO is losing ground to AI-powered search. If ChatGPT, Claude, Gemini, or Perplexity aren’t referencing your brand when people ask questions… you don’t exist. This is your crash course in LLM Engine Optimization (LEO).

🚨 Want Help Rewriting Your Content for LEO?

If you want to turn your blog post, landing page, or newsletter into something ChatGPT might actually cite, I built a free GPT that does just that.

ChatGPT - LLM Optimization Rewriter

Rewrite your content so it gets cited by AI tools like ChatGPT and Perplexity.

chatgpt.com/g/g-685215ba6304819187eeac4a6e0af593-llm-optimization-rewriter

What Is LEO and Why It’s a Big Deal

LLM Engine Optimization is the process of shaping your content so it gets used by language models - not just indexed by Google. The game has changed:

• SEO = ranked links

• LEO = cited answers

AI models don’t send people to your site. They quote you (or they don’t). That means visibility = citation.

And citation means influence, trust, and traffic from AI-native users.

How AI Models Choose What to Cite

When someone asks ChatGPT or Perplexity a question, here’s what happens behind the scenes:

Signal

What the model looks for

Authority

Are you mentioned across credible sources?

Comprehensiveness

Can one of your pages answer the whole question?

Freshness

Is your info current and regularly updated?

Context Match

Does your paragraph directly address the query?

Sentiment

Do other sources reference you positively?

It’s not just about what’s on your site - it’s about your reputation across the internet.

And here’s the kicker: AI tools usually only reference 1–3 sources. So you either make the cut or you don’t show up at all.

How To Structure Your Newsletter for LLM Discovery

Here’s how to future-proof every issue:

1. Start Strong

Put the most important insight in the first paragraph. Think: clear answer first, context second.

✅ “LLM Optimization helps you show up in AI answers. Here’s how to actually do it, step-by-step.”
❌ “AI is changing how we search… and that has some implications…”

2. Use Question-Based Subheadings

Match how people ask questions. For example:

• ❌ “SEO vs LEO”

• ✅ “What’s the difference between SEO and LLM Engine Optimization?”

3. Make Each Section Stand Alone

Every paragraph should work like a mini answer box. No rambling intros - just answer the question and move on.

Good structure:
H2: What Is LLM Optimization?
One-paragraph definition
Bullet points or examples
Optional: link to related post

4. Add a Table of Contents

If your post is over 1,000 words, use a clickable table of contents. This helps LLMs parse your structure and improves user navigation.

Example: Before vs After

❌ Original Newsletter Snippet:

“Let’s talk about AI search. Google’s new SGE feature is rolling out, and it’s changing how we think about content visibility…”

✅ Optimized for LEO:

What Is Google SGE and Why Does It Matter for Content Creators?
Google’s Search Generative Experience (SGE) uses AI to generate answers directly in search results. Instead of showing 10 blue links, it summarizes from trusted sources. If your brand isn’t in that answer box, you’re invisible.

Example:

Bonus Tips for LEO-Optimized Content

• Answer real user questions - use tools like Perplexity, AnswerThePublic, and “People Also Ask” to find phrasing

• Structure your insights like Wikipedia entries: answer > explain > elaborate

• Sprinkle first-party data or frameworks throughout (LLMs love citing original thinking)

• Add “Last updated” dates and keep evergreen content fresh

• Link to trusted sources and get linked from high-signal blogs, roundups, and communities

• Review your server logs to monitor the access patterns of search engines and AI crawlers, such as GPTBot or BingBot, on your website.

What to Do Next (Your LEO Sprint)

Week

Action

1

Run your newsletter through ChatGPT or Perplexity - are you cited?

2

Rewrite your most-read issue with question-style headers

3

Add a definitive “pillar” post to your site (3,000+ words, cited sources)

4

Seed mentions: get featured in a roundup, podcast, or expert quote list

Final Thought

If your name isn’t coming up in AI answers, it’s not because your content sucks. It’s because it isn’t optimized for how AI thinks.

Start with structure. Focus on questions. Create source-ready insights.

Your future traffic will thank you.

If this helped, subscribe to AI the Boring - your no-hype guide to working smarter with AI. Or forward this to someone still obsessing over Google rankings. They’ll thank you in six months. You can also connect with me on YouTube, X, Instagram & LinkedIn.

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The SMARTEST Way to Build AI Apps in 2025 (No Code + MCP)

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Let’s cut through the noise.

If you still don’t have ChatGPT on your phone, what are you doing?

No seriously - what are you actually doing? I’ve heard every excuse by now: “I don’t trust AI,” “I’m too busy,” “I’m waiting to see where it goes.” But let’s call it what it really is: you’re either lazy, or you’re hoping this whole AI thing just blows over.

It won’t.

AI isn’t the future. It’s the right now. Just ask the company in Portugal that launched an AI chatbot and raked in $100 million in sales. Not projections. Not potential. Real results.

So… what’s the difference between them and everyone else?

They actually used the tools.

AI Doesn’t Care About Your Opinion

Here’s the truth: AI doesn’t need your approval. It’s not waiting for you to catch up. It’s already reshaping industries - marketing, real estate, logistics, law, finance. And if you’re sitting on the sidelines because it feels uncomfortable or overwhelming, you're not being cautious. You’re being left behind.

That’s harsh, but it's real. Most people want change without the discomfort of change. They want the benefits of AI without the icky learning curve. But the gap between early adopters and everyone else is widening fast.

You either build with it or get replaced by someone who does.

Your 50-100 Hour Reality Check

Want to know how to stop falling behind? It’s not about buying the right software or finding a unicorn AI employee.

It's about time.

The only real requirement is this: invest 50-100 hours into understanding what AI can do for you, your career, and your business.

Here’s a dead-simple plan to get started:

1. Download ChatGPT (or Claude or Perplexity…just pick one)
   Put it on your phone. Open it daily. Use it for real tasks, not just “fun” prompts.

2. Ask better questions
   Try things like:

   • “How will AI affect real estate?”

   • “What are 5 ways AI can automate customer support for a small business?”

   • “Write me an email pitch for [insert your product].”

3. Build a basic AI workflow
   Pick one repeatable task in your week - writing content, analyzing reports, summarizing meetings - and replace it with AI. Not tomorrow. Today.

You don’t need to understand the backend or write a single line of code. You just need to get in the arena. Use the tools. See what’s possible.

Still Think It’s Optional?

Let’s zoom out.

AI isn’t just doing “tasks.” It’s shaping preferences, decisions, and distribution. Here’s what’s coming:

• You say, “Find me a good local café with oat milk lattes and a quiet vibe to work for a few hours.”

• Your AI checks real-time noise levels, Wi-Fi quality, and menu options - then books you a seat and sends directions.

• You didn’t scroll Google Maps. You didn’t read reviews.

• If your café isn’t known, tagged, or optimized for discovery - you don’t even make the list.

That’s the world we’re heading into. AI will decide, and we’ll thank it for the convenience.

Unless you build brand, build presence, and build systems that play with AI - not against it- you won’t just miss out. You’ll disappear.

Last Excuse: “I’m Too Old”

I hear this one a lot:
“I’m 60, I’ll just ride it out.”
Respectfully…no, you won’t.

Even if you’re not working, AI will impact your life. Your health. Your bills. Your shopping. The sooner you understand how it works, the easier your life will be. Tech doesn’t care about your age. But you should care about your leverage.

Do the Work - Or Get Worked

Look, none of this is about fear-mongering. It’s about urgency. Not panic. Just momentum.

Nobody’s asking you to become an AI engineer. But if you keep avoiding the tools that are rewriting the rules - you’re not protecting yourself. You’re making yourself replaceable.

Don’t be the person saying “I should’ve learned this five years ago.”

Be the one who says “I saw it coming and I adapted.”

You’ve got two things to do this week:

1. Download ChatGPT and start playing.

2. Block out your first 5 hours of AI research.

Then keep going. Stack hours. Build skills. Build leverage.

The train is moving. Time to stop watching it go by - and start driving it.

Cheers, Jagger

If you found this useful, subscribe to AI the boring for more practical AI insights without the hype. You can also connect with me on YouTube, X, Instagram & LinkedIn.

🔥 Just Dropped: New YouTube Video

Quick heads-up - my latest YouTube video is live now! The video is a beginner-friendly tutorial on building AI-powered apps and automation using Lovable (a no-code app builder) and n8n (an open-source automation tool). It walks viewers through creating a complete no-code web app and automation workflow, specifically building an AI Personal Trainer app by integrating tools like GPT-4o or Claude 3.7 with webhooks and AI agents, all without needing coding experience.

Check it out and let me know what you think in the comments!

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It's wild how quickly things change in tech.

Just months ago, businesses were panicking about AI models ingesting their precious website content. Today? The concern has completely flipped. Now the real fear is being invisible to AI systems like ChatGPT.

Let me break down why this matters more than most realize.

The Quiet Revolution in How People Find Things

Traditional search engines give you a digital scavenger hunt. You get links. You click. You scan. You go back. You click another link. Maybe you find what you need after several attempts.

But AI assistants? They skip all that. Ask ChatGPT where to find the best coffee in Melbourne, and it doesn't send you on a link-clicking expedition - it just tells you. Directly. With reasoning.

And here's the thing I've noticed recently: I'm not alone in this shift. My browser history shows a clear pattern - I'm checking Google less and asking ChatGPT more. Every week, the ratio tilts further.

The Shopping Game-Changer

OpenAI's recent shopping integration isn't just a feature update - it's a fundamental shift in how people discover and buy products.

When a user asks about running shoes for flat feet, ChatGPT doesn't just explain what features to look for - it now displays actual products with visuals, descriptions, and links to purchase.

This is massive. Think about it:

• No paid ads cluttering the results

• No SEO games determining visibility

• Just AI-determined relevance to the user's specific situation

The businesses that understand this new reality first will have an enormous advantage.

Your Website Isn't Your Front Door Anymore

Here's the uncomfortable truth: for a growing number of people, your carefully crafted website isn't their first touchpoint with your business.

Their first impression comes from how you're represented inside AI tools like ChatGPT, Claude, or Perplexity.

If you run a beach resort in Bali, your potential customers aren't just Googling "best Bali beach resorts" anymore. They're asking AI assistants about their specific travel needs:

"I want a quiet beach resort in Bali with great food, a spa, and activities for kids."

The AI gives them tailored recommendations based on their criteria. And critically - some resorts will make the list, and others won't.

The Transaction Gap is the Real Opportunity

Let's talk about what happens next.

After ChatGPT recommends that beach resort, the user has two options:

1. Click through to the resort's website, navigate booking pages, enter credit card details, etc.

2. Or simply say, "That sounds perfect. Can you book it for me?"

Right now, most businesses are only set up for option #1. But option #2 is quickly becoming possible - and for users, it's infinitely more convenient.

The businesses that bridge this transaction gap first will have a serious competitive edge. When five comparable options are presented, but only yours can be booked within the conversation flow, guess who wins the business?

Breaking Down How AI Decides Who to Recommend

Understanding how you get recommended by AI is critical. While the exact algorithms are proprietary, we know some key factors:

1. Structured data clarity - How well can AI systems understand exactly what you offer? Businesses with clear, structured data win here.

2. Social proof verification - AI systems look for consistent positive mentions across the web. Are customers talking about you favorably?

3. Specificity advantage - Generic businesses get lost. Those with clear specializations and unique value propositions stand out in AI recommendations.

4. Transactional readiness - As platforms enable direct bookings and purchases, having the technical integration ready will be critical.

Local Businesses Need This Most

This shift affects everyone, but local businesses have the most to gain (or lose).

Take our Bali resort example from earlier. When a tourist asks ChatGPT about the best resort in Bali, appearing in that recommendation list means everything. Being left off could be existential.

The good news is that local businesses can often move faster than corporate giants. While larger companies navigate bureaucracy to adapt, nimble local operations can quickly optimize for AI visibility.

Four Steps to Make Your Business AI-Visible

Here's my action plan for making your business visible in the new AI-first world:

Step 1: Audit Your Digital Footprint

Run experiments now. Ask ChatGPT and other AI tools about your business category, location, and specific offerings. See if you appear, and what information is presented. This is your baseline.

Step 2: Structure Your Online Information

AI systems love structure. Implement schema markup on your website. Ensure your Google Business Profile is complete. Make your offerings crystal clear across all platforms.

Step 3: Build a Consistent Review Strategy

Reviews matter tremendously in how AI tools assess your business. Implement a systematic approach to gathering authentic customer feedback across platforms.

Step 4: Prepare Your Transaction Layer

Even if direct AI transactions aren't fully available in your industry yet, start preparing. Streamline your booking/purchase processes and explore API integrations that will enable seamless AI-initiated transactions.

The Time Advantage Is Massive

The best part about this shift? We're still early.

While everyone else is focused on traditional marketing channels, you have a rare opportunity to establish dominance in the AI recommendation space.

Those who wait until this becomes obvious will be playing catch-up for years. The businesses that become AI-visible now will build compounding advantages as these platforms grow.

The Bottom Line

The question isn't whether AI tools will become major discovery and transaction channels - that's already happening. The question is whether your business will be visible and actionable within them.

This isn't just another marketing channel to add to your mix. It's a fundamental shift in how people find and choose businesses. Miss this, and you might find yourself invisible to an entire generation of customers who start and finish their purchase journeys within AI interfaces.

What are you doing to make your business AI-visible? Hit reply and let me know!

Cheers, Jagger

If you found this useful, subscribe to AI the boring for more practical AI insights without the hype. You can also connect with me on YouTube, X, Instagram & LinkedIn.

🔥 Just Dropped: New YouTube Video

Quick heads-up - my latest YouTube video is live now! The video is a beginner-friendly tutorial on building AI-powered apps and automation using Lovable (a no-code app builder) and n8n (an open-source automation tool). It walks viewers through creating a complete no-code web app and automation workflow, specifically building an AI Personal Trainer app by integrating tools like GPT-4o or Claude 3.7 with webhooks and AI agents, all without needing coding experience.

Check it out and let me know what you think in the comments!

This is the go-to newsletter and community for no-code AI tools, news and productivity insights.

💡 OpenAI just made image generation boringly easy

OpenAI’s new model (GPT-4o) can now generate images natively. No switching models. No extra tools.

Here’s what it can do:

• Create stunning, realistic images in seconds

• Understand and edit images using natural language

• Combine text + image workflows without skipping a beat

This isn’t just fun - it’s infrastructure. Think Canva on steroids, inside ChatGPT.

Here are some examples:

1. YouTube Thumbnails

2. Advertising

3. Infographics

Greg Isenberg is a product advisor to Reddit, the CEO of Late Checkout (a startup studio), and one of the best minds online for spotting early product trends.

This week, he posted this thread on X about monetizing AI-generated images.

Here’s what stood out 👇

1. VisionBoard AI
Describe your 1-year plan:
“Launch a product, speak on stage, lose 10 lbs”
→ It turns your goals into a clean, aesthetic visual collage.
$29 per set. $50/year if you want motivational reminders.
Feels like manifestation, but for product people.

2. Airbnb Photo Enhancer (I tested this one)
Upload your average listing photos.
Get back clean, high-conversion images.
$79/property.
Smart wedge into the 7M+ Airbnb hosts market.
Could grow into full-stack hosting tools.

As mentioned, I tried out this idea and it could well be worth building.

My dining room BEFORE 4o

My dining room AFTER 4o

💭 3 more image-AI startup ideas (inspired by Greg Isenberg’s tweet) that I thought of on a walk today

3. PitchDeck Polisher
Founders upload a rough deck.
Get back a beautifully designed version, AI-enhanced and pitch-ready.
$99/deck. Templates powered by what’s trending in YC, Sequoia, etc.

4. Before & After AI
Creators upload their “before” shot—hair, fitness, skin, room makeover.
AI generates a tastefully aspirational “after” version.
Perfect for coaching funnels, ecommerce, or engagement bait.
$15/image or $40/month for batch processing.

5. Menu Makeover
Restaurants upload a photo of their printed menu.
Get a redesigned, modern version (print and digital).
Bonus: Add AI-generated food photos for each item.
$49/menu. Great wedge into SMB design-as-a-service.

That’s it for this week.
OpenAI shipped. Greg shipped. You?

Expect a new wave of image-first startups.

Cheers,
Jagger

P.S. If you think this newsletter might help someone you know, feel free to forward it along. Sometimes, the right idea starts with the right conversation.

Oh, and do not forget to enter the draw to win a Perplexity PRO membership (closing this Friday). Enter here.

and…here is my latest video on YouTube

How To Use Manus AI To INSTANTLY Generate $1M+ Startup Ideas

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So, Manus AI has been blowing up on X. And it's changing how people think about AI.

What Makes Manus Different?

I've been watching this space closely, and Manus isn't just another AI chatbot. It's an autonomous agent that can:

• Execute complex tasks without you babysitting it

• Interact with your computer (clicking, typing, switching apps)

• Plan and complete multi-step workflows on its own

The crazy part? It can see your screen and act on it. Imagine asking it to build you a website, and it just... does it. No hand-holding required.

This viral video showcases this, detailing how Manus is used to run 50 social media accounts 24/7 automatically:

The X Buzz is Real

People are going nuts over what Manus can do. Some people are using it to analyze stock data and completing what would be weeks of work in under an hour.

Others are having it code entire 3D games from scratch.

The FOMO is so intense that invite codes are selling for 100,000+ yuan ($13,500 USD) on Chinese social platforms. That's how you know something's hitting different.

My Take

Look, we've seen AI tools come and go (I've tested a bunch for this newsletter), but Manus represents something fundamentally new: the shift from "AI assistants" that just chat to "AI agents" that actually DO things in the real world.

It's not just about generating text anymore. It's about execution.

This reminds me of when I was exploring LitRPG as a new frontier for writers - there's this massive opportunity when a technology creates a gap in the market. Right now, there's a gap between what most people think AI can do and what tools like Manus are actually capable of.

Just some of the use cases that can be performed with Manus

What This Means For You

If you're building anything online right now, you need to be thinking about how autonomous AI agents like Manus will change your workflow.

The people who figure out how to leverage these tools first will have an unfair advantage.

What's Next?

I'm going deep on Manus in the coming weeks. I'll be testing it against real business problems and sharing what I learn.

Cheers,
Jagger

P.S. If you think this newsletter might help someone you know, feel free to forward it along. Sometimes, the right tool idea starts with the right conversation.

Oh, and do not forget to enter the draw to win a Perplexity PRO membership (closing next week). Enter here.

and…here is my latest video on YouTube

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Hello builders,

I've got some exciting news to share - I recently got accepted into Perplexity's AI Business Fellowship! Over the next several weeks, I'll be attending exclusive fireside chats and workshops led by some of the top minds in the AI world, including Jensen Huang (CEO of Nvidia), Ali Ghodsi (CEO of Databricks), Aaron Levie (CEO of Box), and Amjad Masad (CEO of Replit).

My plan is to capture the most valuable insights from these sessions, things like the latest AI trends, practical use-cases, no-code strategies, and behind-the-scenes perspectives from these industry leaders, and share everything with you right here in the newsletter and through my YouTube channel.

On top of that, Perplexity has generously provided me with THREE one-month Pro memberships (normally $20/month each), and I want to share them with you. If you're not familiar, Perplexity offers advanced AI tools paired with real-time internet search, making it super helpful for research, productivity, and getting quick, accurate answers to complex questions.

Interested in grabbing one of these memberships?

Here's how to enter:

• Subscribe to my YouTube channel here

• Complete the quick form linked here

I'll randomly select three winners and reach out via email soon.

I'm genuinely excited to dive into these sessions and share what I learn with all of you. Thanks for being part of this journey—I appreciate your support!

Stay tuned for updates, and good luck in the giveaway!

Cheers,
Jagger

P.S. If you think this newsletter might help someone you know, feel free to forward it along. Sometimes, the right tool idea starts with the right conversation.

Connect with me here : )

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Remember when making games required a CS degree and 10,000 hours of practice?

Those days are officially DEAD.

Today I'm showing you how to build a polished, shareable game using AI—with zero coding experience required. And the best part? It's 100% free.

What we're building: An AI/robot-themed shooter game with smooth mechanics, responsive controls, and engaging visuals that you can deploy online and share with anyone.

Time needed: About 30 minutes

Cost: $0

Tools needed: Just Grok and Lovable (both free/have free tiers)

Let's dive in!

The Two-Step Game Creation Process

Step 1: Design Your Game in Grok

Grok's Deep Search function is perfect for planning and designing your game:

1. Sign up for Grok (free)

2. Toggle on "Deep Search"

3. Use this prompt:

Create a highly polished AI robot theme shooter game using p5.js. The game should have smooth mechanics, responsive controls, and engaging visual effects. Design all sprites and assets from scratch—do not use any pre-made or external assets. Final product should feel sleek and refined with an immersive experience for players.

After about a minute, Grok will generate a comprehensive outline including:

• Game overview

• Asset design

• Mechanics and controls

• Visual effects

• Basic code structure

• Enemy AI implementation

Step 2: Turn Your Design into a Working Game with Lovable

1. In Grok, use the "Think" model (not Deep Search) with this prompt:

Create a complete and functional code block for this game. All assets and sprites must be fully original and created within the script. Ensure smooth gameplay mechanics. The final implementation should be immersive and ready to play without any additional files or dependencies.

2. Copy the code Grok generates

3. Go to Lovable (which now integrates with Claude 3.7 Sonnet!)

4. Paste this prompt followed by your code:

I am building a p5.js implementation of an AI robot theme shooting game and I have the code to implement below:

[PASTE YOUR CODE HERE]

At this point, you'll have a functional—but very basic—game. Now comes the magic...

Taking Your Game From Basic to Viral

The initial game will be functional but underwhelming. Here's where Lovable shines.

Add this prompt to transform your game:

I found the gameplay to be too simple and not competitive at all. The environment and enemy robots are absolutely hideous—they barely resemble robots or a stunning battlefield and fail to intimidate me at all. For the game to go viral, redesign the robots and the environment, and make any other tweaks to boost the chances of it going viral.

The transformation is incredible. In just a few iterations, you'll have:

• Beautiful UI with responsive controls

• Health bars and power-ups

• Wave-based gameplay that increases in difficulty

• Significantly improved graphics

Each prompt iteration makes your game better, letting you fine-tune exactly what you want.

Why This Matters

This approach represents a fundamental shift in how we create. Not long ago, game development required years of specialized knowledge. Now, AI tools like Grok 3, Claude 3.7 Sonnet, and Lovable can transform your ideas into reality in minutes.

The barrier to creation has never been lower.

The crazy part? This same approach works for nearly any digital product. Games are just the beginning.

Try It Yourself

Take the game for a spin and see if you make the leaderboard - check it out here!

And if you prefer watching along, here is the full video breakdown:

Play around and see what's possible with just a few prompts.

Once you build your own game, comment on the YouTube video with a link in the comments. I'd love to see what you create!

Cheers,
Jagger

P.S. If you think this newsletter might help someone you know, feel free to forward it along.

Also, watch next:

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The concept is straightforward but powerful:

• 7 hours of content consumption (someone spends 7+ hours with you/your brand)

• 11 interactions (comments, emails, social engagement)

• 4 locations (across multiple platforms: YouTube, LinkedIn, Twitter, podcasts, in-person etc.)

This is how brands build trust and turn cold audiences into high-ticket customers. It’s how elections are won. It’s how unknown founders become the next big thing.

And AI can supercharge this.

1. Founder-Led Marketing Is Non-Negotiable in 2025

If you’re a founder, CEO, or even a solo builder, people need to see you. Not a faceless brand. Not a perfectly curated social media feed. You.

Elections proved this. The 2024 U.S. presidential election cycle saw a massive shift in how candidates built trust. Data from Edison Research showed that over 60% of voters engaged with long-form podcast interviews featuring presidential candidates, surpassing traditional TV interviews.

Trump, in particular, took over the podcast circuit—hitting Joe Rogan Experience, Flagrant, Impulsive, Bussin’ with the Boys, and more. His October interview with Rogan pulled in 52 million views on YouTube alone, making it one of the biggest episodes of the year. Meanwhile, traditional TV struggled to capture the same level of engagement.

Rogan himself called it the first internet election. And if podcasts were the power play for an election, imagine what this strategy can do for founders and businesses.

The biggest B2B deals in 2025 and beyond won’t come from paid ads alone—they’ll come from people trusting a human face.

The era of founder-led marketing is here. AI can’t replace your face, voice, or personal stories. But it can multiply your presence.

• You film one podcast → AI turns it into 50 content pieces used across multiple platforms (e.g., Opus Clip)

• You write one LinkedIn post → AI repurposes it for Twitter, a blog, and an email (e.g., Create a Zapier/n8n/Make workflow → check out my YouTube channel for these)

• You do one customer call that leads into a successful sale → AI summarizes key points into future content.

Most people try to automate connection. Huge mistake. You can’t beat human connection. But you can automate distribution, repurposing, and scaling your voice.

AI should make you omnipresent, not replace you.

2. AI Makes 7,11,4 Easy (If You Use It Right)

Let’s break down how AI fits into the 7,11,4 strategy without making you sound like a chatbot.

7 Hours of Content (AI as a Volume Engine)

You don’t need to record 7 hours of content manually. AI tools like Descript, OpusClip, or Claude 3.5 can take long-form videos and turn them into short-form gold.

Example: I take a 20-minute YouTube video → AI creates 10 TikToks, 3 LinkedIn posts, and a Twitter thread.

11 Interactions (AI as a Research Assistant, Not a Bot)

People don’t want AI-generated replies in the comments. But AI can help you spot high-value interactions, suggest responses, and even summarize key points from discussions.

Example: Instead of a chatbot replying for you, AI flags potential buyers in your audience, suggests high-engagement replies, drafts personalized responses you tweak in 10 seconds, etc.

4 Locations (AI as a Distribution System)

The biggest mistake? Sticking to one platform. The best marketers exist in multiple spaces at once. AI makes this seamless.

Example: I write this newsletter once → AI adapts it for LinkedIn, Twitter, and a YouTube video outline. Now, I don’t need to spend 10 extra hours rewriting content.

3. Your Brand Is the New Product

If you build AI products, here’s a hard truth: Your brand is now as valuable as the product itself.

• If people trust you, they’ll trust your tools.

• If they see you everywhere, they’ll remember your name.

• If you’re constantly in the conversation, they’ll want to do business with you.

The biggest AI founders in 2025 won’t just be coders—they’ll be creators.

Look at Greg Isenberg, Steven Bartlett, Alex Hormozi. Their businesses thrive because they show up, everywhere.

And AI is the cheat code to making that happen at scale.

Final Takeaway: Use AI as Your Megaphone, Not Your Replacement

• AI should make you louder, not less human.

• Founder-led marketing is how you win.

• 7,11,4 is your roadmap—AI just makes it effortless.

I’m doubling down on this in 2025. If you’re a solo builder or an AI founder, this is your moment.

What do you think? Are you using AI to scale your brand yet? Get in touch and let’s talk.

Cheers,
Jagger

P.S. If you think this newsletter might help someone you know, feel free to forward it along.

Also, I want to give a HUGE thank you to everyone who’s subscribed to my YouTube channel. We just broke the 1000 subscriber barrier!

You can connect with me here 👇🏼

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The last couple of weeks have been dominated by AI discussions, and for good reason. We’ve had two major announcements that have everyone talking: DeepSeek R1 and OpenAI’s O3 Mini.

DeepSeek R1 is an AI model out of China that has raised eyebrows—not only because of its impressive performance but also because it was built for under $6 million. That’s a fraction of what it costs to develop similar models. This has sparked plenty of questions: How did they acquire so many GPUs? How did they achieve this level of efficiency? And—perhaps the biggest controversy—did they use OpenAI’s data?

Meanwhile, OpenAI launched O3 Mini, an AI model that’s making waves on its own. It’s smarter, faster, and more affordable than previous models, adding to the new benchmark for AI accessibility. With OpenAI’s influence and DeepSeek’s disruptive pricing, this is an AI showdown worth analyzing.

I broke down both models from a no-code perspective—which one is better for builders, entrepreneurs, and founders looking to integrate AI without writing code?

OpenAI O3 Mini: What’s New?

Sam Altman, OpenAI’s CEO, announced O3 Mini as a model that improves on previous iterations with better reasoning capabilities and cost efficiency. Some highlights:

• Multiple reasoning levels: You can now choose between low, medium, or high reasoning, depending on your needs.

• Competitive performance: On PhD-level science questions, O3 Mini High outperforms its predecessors.

• Large context window: Accepts up to 200,000 input tokens (roughly 150,000 words), with 100,000 output tokens.

• Massively reduced API costs: At $1.10 per million tokens, it’s 93% cheaper than OpenAI’s previous models, bringing it closer to DeepSeek’s industry-disrupting pricing.

On the usability side, developers and no-code builders can leverage O3 Mini through OpenAI’s API, making it a powerful tool for automation and AI-driven applications.

o3-mini costing

DeepSeek R1: A Game Changer?

DeepSeek R1 is making waves primarily because of its low-cost API access. While OpenAI’s models have seen dramatic price cuts, DeepSeek still comes in cheaper at 14 cents per million tokens—an incredible difference from OpenAI’s $15 per million tokens for the 4o model.

But pricing aside, what about performance? We ran a few no-code use cases to compare DeepSeek R1 against O3 Mini. Here’s what we found.

Performance Breakdown: O3 Mini vs. DeepSeek R1

We tested both models across multiple AI-driven tasks relevant to no-code entrepreneurs:

1. AI-Powered No-Code App Outlining

We prompted each model to generate a structured plan for building a job board on a no-code platform like Lovable. The goal was to evaluate clarity, structure, and reasoning ability.

• DeepSeek R1: Took significantly longer to process but provided a very detailed outline, including a structured table of features and potential no-code tools to use.

• O3 Mini (Low & High): Faster responses, with O3 Mini High offering the most refined breakdown. However, it lacked DeepSeek’s detailed integration suggestions.

Verdict: DeepSeek R1 wins on depth, while O3 Mini High offers a more structured and readable breakdown with slightly better clarity.

2. AI-Generated Automations & Integrations

We asked both models to suggest three no-code integrations for streamlining recruitment on the job board build (e.g., notifying recruiters when a candidate applies).

• DeepSeek R1: Provided a step-by-step guide, detailing specific tools (Zapier, Make.com, Airtable, Slack) and how to set up the automation.

• O3 Mini: Suggested similar tools but lacked implementation details, requiring additional prompting for a step-by-step guide.

Verdict: DeepSeek R1 outperforms O3 Mini by making implementation effortless for no-code users.

3. Monetization Strategies for a No-Code Job Board

For builders looking to make money, we asked both models for two monetization strategies tailored to a no-code job platform.

• DeepSeek R1: Suggested pricing tiers, a subscription model, and specific no-code payment tools like Stripe, MemberStack, and Bubble’s native payment features.

• O3 Mini: Suggested similar monetization strategies but lacked direct recommendations on specific tools unless explicitly asked.

Verdict: Both models provide solid strategies, but DeepSeek R1 is more practical by integrating tools into its recommendations.

Final Thoughts: Which Model is Better for No-Code Users?

While O3 Mini excels in speed and structured responses, DeepSeek R1 stands out in implementation detail. If you’re an entrepreneur using AI for automation, DeepSeek R1’s real-world applicability and cost-effectiveness make it a serious contender.

• Best for speed & structured reasoning: O3 Mini (High)

• Best for practical implementation & cost efficiency: DeepSeek R1

As AI continues to evolve, models like these are shaping a future where anyone can build without code. Whether you use DeepSeek or OpenAI, your AI-powered startup is just a few prompts away.

Cheers,
Jagger

P.S. If you think this newsletter might help someone you know, feel free to forward it along.

Plus, here’s a new video that might interest you.

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Let’s be real…AI and crypto are having a moment, but not all moments are good ones. Every other day, some new AI-powered memecoin pops up, promising the moon but delivering vapor. Trading bots flood the market, pumping and dumping tokens at lightning speed, while “AI-driven” DeFi platforms claim to be the future of finance but can’t even manage basic security.

It’s exhausting. And if you’ve been paying attention, you know the AI x crypto space is a mess of hype and half-baked ideas.

That’s why I want to cut though the noise. Focusing on what actually matters…the real, tangible value AI can bring to crypto. Not the marketing buzzwords, but the real innovations that will make crypto safer, smarter, and more efficient.

Why listen to me? I have a Master’s in Applied Science and have been academically published in AI and blockchain convergence. My research focused on how AI can improve decentralized systems, and visa and versa, not just theoretically but in real-world applications. You can read about that here.

What Are AI Agents, Really?

Forget the overhyped trading bots and scammy “AI tokens.” AI agents in the scope of blockchain technology are autonomous programs that execute onchain actions without human intervention. Unlike simple bots that follow pre-set instructions, AI agents learn, adapt, and optimize over time.

They combine large language models (LLMs), autonomous decision-making frameworks, and blockchain integration to handle complex tasks like:

• Automating DeFi strategies

• Optimizing governance participation

• Managing gaming economies

• Detecting fraud before it happens

All of this is explained below.

Why Should You Care?

Because AI agents are about to change how we interact with crypto…not in the hypothetical, someday-in-the-future sense, but in real ways right now.

✅ Frictionless Onchain Actions – Say goodbye to manually swapping tokens, bridging assets, or micromanaging yield farming. AI agents automate these tasks with efficiency.

✅ 24/7 Market Participation – Unlike humans, AI agents don’t sleep. They track markets in real time, identifying trends and providing advice on executing transactions without delay.

✅ Risk Mitigation – AI-powered security layers detect scams, block malicious contracts, and prevent costly mistakes before they happen.

✅ Smarter Governance – AI helps analyze governance proposals, forecast outcomes, and make informed decisions, taking the guesswork out of decentralized participation.

✅ DeFi Optimization – AI-driven DeFi agents maximize lending, borrowing, and yield farming strategies to ensure users get the best returns without constantly monitoring the market.

Crypto is complex. AI makes it simpler and safer.

The Use Cases That Actually Matter

There’s a lot of AI x crypto nonsense, so let’s focus on the innovations that aren’t garbage:

🚀 DeFi x AI (DeFAI) – Decentralized Finance (DeFi) lets people borrow, lend, and trade without banks, but managing it is a nightmare. AI can:

• Automate lending strategies

• Optimize yield farming

• Detect fraudulent transactions before they happen

🏛️ Governance x AI – Decentralized projects let token holders vote on decisions, but most people don’t have time to read every proposal. AI can:

• Analyze proposals and summarize key points

• Predict voter sentiment based on onchain activity

• Rank governance options by potential impact

🎮 Gaming x AI – Blockchain gaming is exploding, but it’s still full of clunky mechanics and broken economies. AI can:

• Power intelligent NPCs that react dynamically to players

• Balance in-game economies in real time

• Automate NFT trading for optimal asset management

💰 PGF x AI – Public Goods Funding (PGF) is about funding open-source crypto projects. AI can:

• Automate grant distribution based on real impact

• Detect projects that are just cash grabs

• Optimize retroactive funding for proven initiatives

🌱 ReFi x AI – Regenerative Finance (ReFi) is about using crypto for sustainability. AI can:

• Predict carbon credit demand

• Automate eco-friendly project funding

• Ensure real-time transparency in environmental impact

🐶 Memecoins x AI – Yes, even memecoins can benefit. AI can:

• Track social sentiment for trend predictions

• Detect potential rug-pulls before they happen

• Identify real utility in the memecoin space (rare, but possible)

The Challenges No One Talks About

AI and blockchain together sound great, but it’s not all sunshine and 100x gains. Here’s what’s standing in the way:

⚠ Scalability – AI requires a ton of computation, and blockchain isn’t built for high-speed data processing.

⚠ Regulatory Landmines – Who owns AI-generated financial decisions? Is AI trading even legal? No one knows yet.

⚠ Ethical Black Holes – AI making financial decisions without oversight could lead to manipulation or unintended market crashes.

⚠ Too Much Noise – The sheer number of “AI-powered” projects that are actually just rebranded bots makes it hard to find what’s real.

The Bottom Line

AI isn’t just another crypto trend. It’s the missing piece that makes blockchain actually usable at scale. But separating real innovation from AI-generated garbage is the challenge.

Crypto’s next big shift will be AI-driven. Will you adapt, or get left behind?

Cheers,
Jagger

P.S. If you think this newsletter might help someone you know, feel free to forward it along.

Plus. Another video is out. Automate anything with DeepSeek AI and Make.com

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DeepSeek just pulled a classic David vs. Goliath move on the AI giants…and it sent shockwaves through the entire tech industry.

Here’s the deal: DeepSeek, a Chinese AI startup, launched a language model that’s dirt cheap compared to OpenAI or Google. And by “cheap,” I mean 27x cheaper.

→ $0.14 per 1M input tokens vs. OpenAI’s $15.
→ $0.55 per cached tokens vs. OpenAI’s $7.50.

Feature

OpenAI o1

DeepSeek-R1

Input API Price

$15.00 / 1M tokens

$0.14 / 1M tokens

Cached Input API Price

$7.50 / 1M tokens

$0.55 / 1M tokens

Output API Price

$80.00 / 1M tokens

$2.19 / 1M tokens

This kind of pricing flips the entire AI economics model upside down. Investors freaked out. Stocks tanked. And $2 trillion evaporated from the market in a single trading day.

Why? Let’s break it down in simple terms:

Big tech’s massive valuations are built on the idea that they control AI innovation. They charge premium prices because they “own the game.” But DeepSeek just said, “Hold my beer,” and delivered top-tier performance at bargain-bin prices.

This scared the market for three reasons:

1. Nvidia relies on AI companies buying expensive GPUs. If AI tools get cheaper, their customers buy less hardware.

2. OpenAI and Google’s margins come from premium pricing. DeepSeek just blew that model up.

3. Barriers to entry are disappearing. If every startup can build powerful AI tools at a fraction of the cost, the whole market changes.

The fallout?
→ Nvidia’s stock dropped 12%.
→ The Nasdaq futures slid 4%.
→ $2 trillion erased from market value overnight.

Nasdaq 100 Stock Price (as at January 28th)

Nasdaq 100 Stock Price (as at January 28th)

The Real Lesson: It’s Not About AI Models

While DeepSeek and the big players like OpenAI and Microsoft duke it out over benchmarks, the real game isn’t who builds the biggest engine…it’s who builds the best experience.

ChatGPT didn’t win because it had the most powerful model. It won because it became so easy to use that millions of people integrated it into their lives.

This is where the value is shifting:
→ It’s not about who has the cheapest or fastest model.
→ It’s about who creates tools people reach for without thinking.

DeepSeek showed that AI is becoming a commodity. The companies that win will be the ones who make it simple, seamless, and part of daily life.

What Builders Should Take Away

If you’re building in the AI space, here’s what you should focus on:

1. Think workflows, not algorithms. How does your product fit into what people do every day?

2. Simplicity wins every time. Nobody cares about complexity—they care about results.

3. Solve real problems. AI isn’t just tech; it’s a tool. Make it solve something tangible.

DeepSeek didn’t just disrupt AI pricing—it reminded us where the real value in this space lies: bridging the gap between advanced tech and usability.

The question isn’t “How strong is your AI?” It’s “How much do people love using it?”

Are you building the engine or the steering wheel?

Cheers,
Jagger

P.S. If you think this newsletter might help someone you know, feel free to forward it along.

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Oh and talking about building…I built an entire AI-powered personal trainer app over the weekend using only 4 tools…Lovable, Supabase, Stripe and Chatling.ai. Check out the video below.

STEAL THIS: Turn Your AI Idea into an App with Lovable (No Code Needed!)