Untitled

[talhaaiautomation]

Google Antigravity + Hermes Agent = 1000+ Leads/Day!

Every freelancer, agency owner, and consultant eventually runs into the same problem.

Finding clients is exhausting.

Not because there aren't enough businesses that need help. There are millions of them. The real challenge is finding the right businesses at the right time and reaching out before your competitors do.

For a long time, lead generation felt like a job of its own. Hours spent searching Google Maps, checking websites, collecting contact information, organizing spreadsheets, and trying to figure out who might actually need your services.

Then I realized something:

Why was I doing this manually when AI could do it for me?

Today, I have an AI agent that wakes up before I do.

Every morning, it automatically finds new local businesses that match my criteria, adds them to a spreadsheet, and prepares them for outreach.

But the most interesting part isn't the lead generation.

It's what happens after.

The Problem With Traditional Outreach

Most service providers make the same mistake.

They find a business and immediately send a message:

"Hey, I build websites. Are you interested?"

The business owner ignores it.

Another generic pitch lands in their inbox.

Another sales message gets deleted.

The problem isn't your service.

The problem is that you're asking people to imagine the value before they've seen it.

Most business owners don't care about websites.

They care about getting more customers.

They care about looking professional.

They care about not losing business to competitors.

If you can show them the result before they ever become a customer, the conversation changes completely.

That's the idea behind this system.

Instead of Pitching a Website, I Build One First

Let's say my AI agent finds a dental clinic that doesn't have a website.

Most people would call the clinic and offer web development services.

I take a different approach.

Using AI, I automatically create a custom website for that clinic before I ever contact them.

The website already contains:

→ Their business name → Their phone number → Their location → Their Google Maps information → Relevant images → Branding based on their business

When I finally reach out, I don't say:

"I can build you a website."

I say:

"I already built one for you."

That single difference changes everything.

Instead of discussing possibilities, we're discussing something real.

The business owner can click a link and immediately see what their online presence could look like.

Try to explore my Youtube channel and Facebook Page.(click the text)

The Two Free AI Tools Behind the System

The entire workflow runs on two primary AI tools.

The first is Hermes Agent.

The second is Google Antigravity.

Hermes Agent handles lead generation and automation.

Google Antigravity handles website creation.

Both can be used for free, and together they create a surprisingly powerful client acquisition machine.

The workflow is simple:

→ Find businesses that match specific criteria. → Store their information in Google Sheets. → Generate customized websites automatically. → Publish the websites online. → Add the demo website links back into the spreadsheet. → Use those websites during outreach.

Once everything is connected, the process becomes largely automated.

Why I Host My AI Agents on a VPS

Running AI agents locally works.

But there's a problem.

When your laptop shuts down, the automation stops.

That means your lead generation system only works when your computer is running.

To solve this, I host my AI tools on a VPS.

This allows the agents to work continuously in the background, even while I'm sleeping.

My VPS hosts multiple AI tools simultaneously.

The setup is surprisingly simple because many hosting providers now offer one-click deployments for popular AI projects.

Instead of worrying about infrastructure, I can focus on results.

The AI agents continue working around the clock.

Teaching Hermes Agent to Find Leads

The instruction I gave Hermes Agent was straightforward:

Find five local businesses every day that don't have websites.

Then collect important information such as:

→ Business name → Phone number → Google Maps URL → Ratings → Additional business details

Finally, store everything in a Google Sheet.

At first, I focused specifically on cafes in Bangalore.

Why cafes?

Because many local cafes still operate without dedicated websites.

They're easy to identify, easy to contact, and often benefit significantly from a professional online presence.

The AI's job wasn't just finding cafes.

Its job was finding opportunities.

Scraping Google Maps Without Doing It Manually

To gather business data, I connected Hermes Agent to a scraping service.

This allowed the AI to search Google Maps and collect business information automatically.

The interesting part was what happened during the first run.

The AI discovered:

→ 1,249 cafes → 653 cafes without websites

At first, I was excited.

Then I opened my spreadsheet.

There were only five leads.

For a moment, I thought something had gone wrong.

Then I realized the AI had followed my instructions perfectly.

I had specifically asked for five new leads per day.

The agent found hundreds of opportunities but only added the five it was instructed to deliver.

That small mistake taught me something important about AI systems:

Most errors aren't actually AI failures.

They're instruction failures.

The AI did exactly what I asked.

Not what I intended.

The Reality of Building AI Workflows

One thing people don't talk about enough is troubleshooting.

AI workflows rarely work perfectly on the first attempt.

My Google Sheets connection broke during setup.

The agent couldn't properly update the spreadsheet.

Data wasn't syncing correctly.

Instead of manually debugging everything, I simply asked the AI what was wrong.

It walked through the issue, identified the connection problem, and suggested a fix.

After refreshing the connection and reconnecting the credentials, everything started working again.

This is becoming one of the most underrated skills in AI:

Knowing how to talk to your tools.

Many technical problems can now be solved by asking the AI itself to explain what happened.

Turning Leads Into Demo Websites Automatically

Once the spreadsheet was populated, the next step was creating websites.

This is where Google Antigravity entered the workflow.

I shared the spreadsheet with the AI and gave it a simple objective:

Analyze each business.

Understand what they do.

Create a customized website for them.

Use relevant images.

Pull information from Google Business Profiles when possible.

Generate missing assets when needed.

Publish each website online.

Return the URL to the spreadsheet.

That's it.

No coding.

No design work.

No manual page building.

Just a prompt.

Publishing Websites for Free

After generating the websites, the AI automatically hosted them using GitHub Pages.

This is one of the most useful free resources available for simple website deployment.

The AI generated the site, pushed the code to GitHub, and created a public URL.

The final spreadsheet contained:

→ Business information → Contact details → Google Maps links → Demo website URLs

Everything in one place.

Now every lead wasn't just a lead.

It came with a personalized demonstration.

The Results Were Better Than I Expected

The first demo website was built for a board game cafe.

The AI pulled business information from Google Maps and transformed it into a clean, professional website.

It included:

→ Ratings → Directions → Contact information → Featured offerings → Business details → Location map

The site looked like something a small agency might spend hours creating.

The second website was even more impressive.

The design felt modern and polished.

The AI incorporated business details, popular menu items, imagery, operating hours, customer-facing content, and location information.

Most importantly, it felt personalized.

It didn't look like a generic template.

It looked like a website built specifically for that business.

That's exactly what makes these demos effective.

Why This Approach Works

Business owners don't buy websites.

They buy outcomes.

Showing someone a finished website immediately removes uncertainty.

They don't have to imagine what you'll build.

They can see it.

They can interact with it.

They can picture their customers using it.

That dramatically lowers resistance.

Instead of convincing someone to trust your abilities, you're demonstrating your abilities upfront.

The conversation becomes much easier.

The Bigger Lesson

The most valuable thing about this workflow isn't the lead generation.

It's the leverage.

A single person can now do what previously required an entire team:

→ Research businesses → Qualify leads → Collect data → Organize outreach → Build demo assets → Publish websites → Prepare sales materials

And much of it can happen automatically.

The businesses haven't changed.

The opportunities haven't changed.

What has changed is the cost of execution.

The people who benefit most from AI aren't necessarily the best programmers.

They're the people who learn how to combine tools into systems.

Because once you stop thinking about individual tasks and start thinking about automated workflows, something interesting happens:

You stop looking for clients one at a time.

You build machines that find them for you.

Here are 20 Facebook hashtags tailored to maximize reach for your AI + automation + money-making article:

[talhaaiautomation]

How I use Al agents to make money (Vibe Marketing Tutorial)

I didn’t realize marketing had changed this much until a simple tweet started getting more attention than most polished campaigns I’d ever seen.

“Vibe marketing is the new marketing.”

At first, it felt like another buzzword riding the wave of “vibe coding.” But the more I looked at what was actually happening underneath, the more I realized something uncomfortable:

Marketing is no longer a team activity.

It’s becoming a system activity.

And most people haven’t adjusted yet.

The shift no one is fully prepared for

If you’ve heard of vibe coding, you already understand part of the story. Tools like Replit, Bolt, and Lovable have made it possible to build software from simple prompts instead of traditional engineering workflows.

But vibe marketing is something different.

It’s not about building products.

It’s about building marketing machines.

Machines that don’t sleep, don’t wait for approvals, and don’t depend on a team of specialists working in silos.

And the surprising part is this:

The people who understand this shift early won’t just market better. They’ll operate on a completely different level of leverage.

Why this is happening now

This didn’t appear out of nowhere. Three things converged at the same time:

First, AI finally became good enough at real marketing tasks. Not just writing text, but analyzing data, generating angles, summarizing research, and executing workflows.

Second, vibe coding tools removed the barrier for non-developers. You no longer need to “know how to build” to automate complex systems.

Third, custom tooling costs collapsed. What used to require engineering teams can now be stitched together in hours with APIs and AI agents.

Individually, these changes are interesting.

Together, they change what a marketing “team” even means.

Try to explore my Youtube channel and Facebook Page.(click the text)

The old world vs the new world

Traditional marketing teams looked like this:

→ SEO specialists → Copywriters → Designers → Analysts → Media buyers

Each person worked in their own lane. Information moved slowly. Campaigns took weeks. Everything required coordination across Slack threads, meetings, and revisions.

That structure worked when execution was expensive.

But now?

A single marketer with taste can outperform that entire stack—if they know how to orchestrate AI systems.

Because the new model looks like this:

→ One strategist → AI agents running 24/7 → Continuous testing of angles → Real-time iteration of campaigns

Instead of “launch and wait,” it becomes “observe and adapt instantly.”

And that changes everything.

The mindset shift: marketing becomes high-frequency execution

One idea that stuck with me came from a conversation with my co-founder, James.

He described it like this:

Marketing will start to resemble high-frequency trading.

Agents will monitor signals constantly. They’ll surface opportunities in real time. Campaigns will adjust dynamically based on feedback loops instead of static planning cycles.

In that world, you’re not a campaign manager anymore.

You’re a portfolio manager of attention.

Allocating effort where the signals are strongest.

The tools that make this possible

To understand vibe marketing, you need to understand the stack behind it.

This is not theory. It’s already being used.

Vibe coding tools

→ Replit → Bolt → Lovable

These tools let you generate functional websites and prototypes from prompts. For example, you can describe a product for new homeowners with subscription access and payment flexibility—and get a working version instantly.

That alone changes how fast marketing experiments can be built.

Workflow and agent tools

→ N8N → Gumloop → Lindy

These are where things get interesting.

They allow you to build automated systems that connect scraping, AI reasoning, spreadsheets, and messaging into one loop.

For example:

→ scrape a website → analyze content with AI → write results into Google Sheets

Or:

→ monitor Reddit discussions → extract patterns → generate content ideas automatically

Gumloop is more beginner-friendly, while N8N is more powerful but technical.

But the direction is the same: systems, not tasks.

AI orchestration layer

→ Claude → OpenRouter → GPT models

Claude is often used for writing and structured reasoning. OpenRouter allows you to access multiple models through one interface—choosing different models depending on the task.

This matters because not all models are equal at everything.

Some are better at analysis. Some at writing. Some at reasoning.

Vibe marketing is about assigning the right intelligence to the right job.

Visual and content generation

→ Sora → ChatGPT image generation → Leonardo AI → Kling AI → ElevenLabs

This is where content creation becomes almost unfair.

You can now:

→ generate scripts → create visuals → animate scenes → produce voiceovers

And stitch them into video content that would have previously required a production team.

One experiment I saw involved creating Pixar-level videos using AI tools alone. Script generation, visuals, animation, voice—all automated or semi-automated.

The result wasn’t perfect.

But it was fast enough to compete.

And speed is often the deciding factor in distribution.

The scorecard problem no one talks about

One of the most practical things I’ve seen is a simple idea:

Create a scorecard for AI models.

Different models perform differently depending on the task:

→ blog writing → analysis → research → coding → ideation

For example, some models are surprisingly strong at blog structure and tone consistency but weaker at creative ideation. Others are great at reasoning but inconsistent in writing style.

The key insight is simple:

If you don’t map capabilities, you will randomly use tools instead of strategically deploying them.

And in vibe marketing, randomness kills leverage.

The basic workflow stack

Most systems start with a simple foundation:

→ N8N for automation → Google Sheets for storage → AI models for reasoning → OpenRouter for model routing → data sources like Reddit, YouTube, or social platforms

From there, everything becomes a loop:

Pull data

Transform with AI

Analyze outputs

Apply insights

Repeat continuously

That loop is the core of vibe marketing.

Real workflows people are already building

Once you understand the stack, the applications become obvious.

1. Content monitoring system

→ scrape Reddit or niche communities → detect trends and discussions → summarize insights → generate content ideas

Instead of guessing what to write about, you’re extracting demand signals directly from conversations.

2. Competitor alert system

→ monitor competitor posts → track engagement patterns → send alerts when something spikes

Marketing becomes reactive in real time instead of delayed analysis.

3. Email and meeting summarizers

→ process incoming emails → generate summaries → extract action items

This already replaces layers of manual coordination work.

4. One-click CRM system

A more advanced workflow looks like this:

→ trigger browser extension → scrape a website or YouTube transcript → extract social links and context → analyze relevance → generate outreach message → store in Airtable → send weekly summary

What used to require a junior team member is now automated.

Content creation at scale

One of the most powerful systems is content scaling.

Here’s how it works:

→ extract ideas from Reddit → generate content angles with AI → store in Google Sheets → expand into scripts or posts using a model like GPT-4-class systems

The key idea is not automation alone.

It’s idea extraction at scale.

Because ideas are the real bottleneck, not writing.

E-commerce automation example

Another system takes Amazon SKUs and runs them through multiple AI agents:

→ analyze reviews → extract keywords → identify search intent → generate product descriptions → create titles and backend terms → produce image overlay copy

The output is a fully structured listing optimized for conversion.

What used to take a team becomes a single automated pipeline.

AI-driven newsletter system

One of the more extreme examples:

→ topic selected from Google Sheets → deep research via AI tools → writing generated by models → images created via generative tools → final newsletter assembled automatically

The concern people raise is always the same: “this will just be AI slop.”

And sometimes it is.

But when the system is guided with strong prompts, constraints, and human oversight, it can produce surprisingly valuable output at scale.

The deeper shift: from executors to orchestrators

This is where everything connects.

The future marketer is not someone who writes every post or builds every campaign.

It’s someone who designs systems that do it for them.

The role evolves from:

→ doing marketing tasks to → designing marketing intelligence systems

And once that shift happens, scale is no longer tied to headcount.

A team of five can operate like a team of fifty.

Not because they work harder.

Because they orchestrate better.

Where this is heading

The next phase of marketing won’t be isolated tools doing isolated tasks.

It will be interconnected AI systems sharing context:

→ agents talking to agents → workflows triggering workflows → systems optimizing other systems

Marketing goals will become direct instructions like:

“Generate 50,000 organic visitors per month.”

And the system will decide how to get there.

In real time.

Adapting continuously based on what works.

That’s the direction this is moving.

And it’s moving faster than most teams are reorganizing for it.

Final thought

There was a time when marketing was about creativity.

Then it became about distribution.

Now it’s becoming about systems.

And the uncomfortable truth is this:

The best ideas won’t win by default anymore.

The best systems will.

And once you see marketing through that lens, it becomes hard to unsee what’s changing underneath everything.

[talhaaiautomation]

I Tried Selling Al Websites to International Clients (Real Results)

$1 = ₹95.

That’s the first thing that hit me that morning.

Not as bad news—but as an opportunity I couldn’t ignore.

Because when your currency weakens this much, something interesting happens that most people miss entirely: earning in dollars suddenly becomes one of the fastest ways to change your financial reality.

And I kept thinking about a simple question:

If I can already make money in India by finding clients for basic websites… what happens if I take the exact same skill and point it at international clients?

Not theory. Not motivation. Just execution.

A while ago, I had already tested a simple version of this idea in India. It was almost embarrassingly straightforward: I used Google Maps to find local businesses without websites, used ChatGPT to generate basic business info and structure, and then reached out directly.

That small experiment made around ₹25,000.

Nothing fancy. No ads. No funnel. Just raw outreach.

But this time, I wanted to push it further.

Could the same system work internationally—where clients pay in dollars instead of rupees?

And more importantly, could AI reduce the friction so much that the “technical skill” part almost disappears?

That became the experiment:

→ Find international clients → Build simple AI-powered websites for them → Try to close at least $1,000 in revenue → Test which type of business actually responds best

And then I added one more twist:

I wouldn’t just test one niche.

I would test three completely different business types and compare which one converts fastest.

The Simple System Behind It

Before anything else, I needed three things to be clear:

First, the country.

I picked Canada—not for any complicated reason, but because freelancers already serve that market heavily, which means businesses are used to paying for digital services.

Second, the type of clients.

I narrowed it down to three categories that research consistently shows are strong buyers of websites:

→ Home contractors → Cleaning companies → Doctors and healthcare professionals

Different industries. Different psychology. Different budgets.

But all of them share one thing: they rely heavily on trust and visibility. A good website directly impacts their income.

Third, outreach.

In India, this used to be simple. I would open Google Maps, find businesses without websites, and call them directly.

But internationally, that doesn’t work the same way for obvious reasons.

So I switched platforms.

Instagram became the main channel.

I would search for businesses, filter by low followers and low engagement (usually newer businesses), and assume something important:

If they are new, they likely don’t have a strong website—or any website at all.

That’s where the opportunity sits.

Niche 1: Home Contractors (The First Attempt)

Home contractors are interesting.

They usually have high-ticket services, but they are not deeply technical. That combination often makes them good potential clients.

So I started there.

I searched for contractors in Toronto and began filtering accounts that looked small, new, or underdeveloped.

Then I started messaging.

At first, I used a straightforward approach:

“Hey, are you taking on new clients right now?”

The idea was simple. Instead of pitching a website immediately, I wanted a response first. If they replied, I could steer the conversation later.

But then I experimented.

I tried humor.

I tried slightly flirty, playful messages inspired by what sometimes works on Twitter/X:

→ “Hey Lucas, you handsome fellow, taking new clients?” → “Hey Callum, you cutie, are you open for work right now?”

It sounds ridiculous in hindsight, but I wanted to test attention patterns.

The result?

Nothing.

No replies.

Zero engagement.

And that was the first reality check: attention does not equal interest. Especially in cold outreach.

So I moved on.

Try to explore my Youtube channel and Facebook Page.(click the text)

Niche 2: Cleaning Companies (The Turning Point)

Cleaning companies are different.

They don’t just need visibility—they need systems.

Booking automation. Proof of work. Before-and-after visuals. Trust signals.

In other words: a website actually solves real operational problems for them.

So I changed the strategy completely.

Instead of pitching with words, I decided to lead with something more powerful:

A sample website.

This is where AI changed everything.

I went to Claude and asked it to generate a structured prompt for a cleaning company website. I refined it further by adding a critical section:

→ Before and after showcase section

That detail mattered because cleaning services are visual proof businesses. Without it, the website feels incomplete.

Then I took that prompt and pasted it into a vibe coding platform (I personally used Hostinger Horizons).

Within minutes, something interesting happened.

The website built itself.

Not metaphorically. Literally.

A clean, minimal landing page appeared with:

→ Service structure → Testimonials section → Pricing layout → Before-and-after slider animation → Contact flow

I hadn’t designed anything manually.

AI handled it end-to-end.

I only provided direction.

And that changed the entire economics of outreach.

Because now I wasn’t saying “I can build you a website.”

I was saying “I already built one for you.”

Then I went back to Instagram.

Same filtering strategy: low followers, low engagement, likely new businesses.

And I changed the message:

“Hey, are you looking for new clients? I built a high-converting website for a cleaning business and thought it might help you book more customers. If you like it, we can customize it for your business.”

Then I attached the link.

No long pitch. No overthinking.

Just proof.

I sent this to 20–25 cleaning companies.

And for the first time, something happened:

There was interest.

Not immediate payment. Not a closed deal.

But replies asking about customization, pricing, and how it works.

That alone was a major shift compared to contractors.

Because now the conversation had started.

Niche 3: Doctors and Healthcare Professionals (The Most Powerful One)

Doctors are a different category entirely.

High budgets. High trust requirement. High emphasis on credibility.

Which means one thing:

A generic website doesn’t work.

So I changed the approach again.

Instead of sending a sample website, I decided to create fully personalized websites for each doctor.

Yes, it takes more effort—but AI reduces that effort dramatically.

Here’s the workflow:

I gathered details from Instagram profiles—name, specialization, content style, visuals.

Then I fed everything into Claude and asked it to generate a custom website prompt tailored to that specific doctor.

Then I used the same AI website builder again.

Within minutes, each doctor had a personalized website designed around their identity.

Not a template.

A representation of their practice.

Then I didn’t just DM them.

I added context:

First, I complimented their content.

Then I explained why I built the website specifically for them.

Then I attached the link.

And on top of that, I commented on their recent posts as well, just to increase visibility.

It wasn’t just outreach anymore.

It was layered visibility.

The Reality of 50 Cold Messages

After everything:

→ 25 contractors messaged → 20 cleaning companies messaged → 5 doctors messaged

Total: 50 cold DMs.

Results were uneven.

Contractors: zero responses Cleaning companies: multiple replies, some interest Doctors: mixed engagement, at least visibility and acknowledgment

But one thing stood out clearly.

A single cleaning company responded seriously.

They didn’t just say “interested.”

They started sending voice notes.

They asked for:

→ Pricing structure → Customization changes → Social media integration → Removal of standard packages → More flexible quoting system

That was the first real signal.

Then the conversation moved to WhatsApp.

And that’s where things got serious.

They shared branding assets, logo, service structure, even images they wanted included on the website.

Now it wasn’t outreach anymore.

It was a client project.

The Final Offer and Negotiation

Once the revisions were made using the same AI workflow, the website was updated again:

→ Brand colors adjusted → Logo integrated → Services page rebuilt → Pricing structure customized → Contact buttons linked properly

The entire update cycle took a single prompt change.

That’s the part most people miss.

This isn’t “web design” in the traditional sense anymore.

It’s prompt-driven product iteration.

When the updated version was sent, the client’s response was simple:

They had previously paid around $400 for a website.

But current market expectations were closer to $600–$700.

Still, they weren’t looking for the cheapest option.

They wanted something professional and reliable.

We positioned the pricing between $400–$500.

And they didn’t push back.

That became the closing range.

What Actually Happened in This Experiment

At the end of the test, the numbers were simple:

→ 50 cold DMs sent → 1 serious client conversation → 1 active deal in negotiation at $400–$500

That’s it.

No viral success. No instant scaling system.

But something more important was proven.

The combination of:

→ AI-generated websites → Rapid personalization → Direct cold outreach → Platform targeting (Instagram)

actually works.

Not perfectly.

But predictably enough to build on.

The Real Insight Most People Miss

This wasn’t really about websites.

It was about leverage.

A few years ago, building even one decent website required technical skill, time, and iteration.

Now it takes a prompt.

And when creation becomes this cheap, the real skill shifts:

Not building.

But distribution.

Who you reach.

How fast you reach them.

And how relevant your offer feels in the first 10 seconds.

That’s the game now.

Not coding.

Not design.

Not even tools.

Just attention + speed + relevance.

And once that clicks, the entire idea of “starting a service business” changes completely.

Because suddenly, one person with the right system can operate like a small agency.

Not by scaling effort.

But by compressing execution time.

That’s the part that actually matters.

Here are 20 strong Facebook hashtags tailored for your article (AI + freelancing + money + startups + cold outreach):

[talhaaiautomation]

$215M AI CEO: How I'd Build a Profitable Al Startup in 30 Days (2026 Playbook)

The most dangerous moment in a startup isn’t when nothing works.

It’s when something looks like it works.

A polished demo. A few impressed friends. A prototype that feels almost magical when you show it on a call.

That’s usually the point where founders start believing they’re building a business.

But in reality, they might just be building a very convincing illusion.

The first 30 days of starting again

If Yan Zhang had to start from zero again, the first 30 days wouldn’t be about branding, scaling, or even building a “real product.”

It would be about something far more uncomfortable: aggressively searching for proof that people actually care enough to change behavior.

Yan is the co-founder and CEO of Opus Clip, an AI tool that turns long-form content into viral short clips. In just a few years, the company grew into one of the fastest-scaling AI startups in the world, reaching tens of millions of users and a multi-hundred-million-dollar valuation.

But none of that started with a product that looked like a product.

It started with experiments that most people would consider “not real startups.”

And that distinction mattered more than anything else.

The feature that accidentally became a company

During the early days—right around COVID—Yan and his team didn’t set out to build a viral video clipping tool.

They built a live-streaming product.

Nobody cared.

Users didn’t stick. The product didn’t resonate. It was one of those situations where everything feels like effort, but nothing feels like traction.

Then something unexpected happened.

Inside the live-streaming tool, there was a small feature: clipping highlights from long videos.

That feature started showing early signs of life.

Not explosive growth. Not hype.

Just small signals that people actually wanted it.

And at the same time, OpenAI models were rapidly improving, making video understanding and transformation suddenly more feasible.

That combination changed everything.

The team didn’t “decide” to pivot in a dramatic boardroom moment.

They followed the signal.

That small feature became the foundation of what later turned into Opus Clip.

Try to explore my Youtube channel and Facebook Page.(click the text)

The uncomfortable truth about early product validation

Most founders look for validation in the wrong place.

They track dashboards. They obsess over metrics. They build interfaces before they understand demand.

Yan’s team did the opposite.

They engineered outcomes manually.

Instead of building a polished product first, they:

→ Took raw videos → Edited clips using AI and manual effort → Sent finished results directly to potential users via email

There was no product to “use” yet. Only results to react to.

The response rate was over 60% positive feedback.

That changed the entire direction of the company.

Because at that point, something important became obvious:

People didn’t care about the tool.

They cared about the outcome.

The Discord bot phase: where real signals appear

After that early validation, the next step still wasn’t a polished SaaS product.

It was a Discord bot.

No interface. No fancy onboarding. No traditional UX.

Just a system where creators could drop content and get clips back.

The goal wasn’t scalability.

It was learning.

And what they observed inside that messy environment mattered more than any analytics dashboard:

→ How often users came back → How they talked about the outputs → What they complained about → What they started requesting repeatedly

At scale, hundreds of creators were using it inside Discord.

For most content tools, weekly usage is normal. Creators typically upload long-form content, generate clips, and post them over time.

But when some users started returning multiple times a week—or even daily—that was a signal that couldn’t be ignored.

Even more important than behavior was language.

Users weren’t asking for “features.”

They were asking:

“How do I get more clips like this?” “Why is my quota limited?” “Why can’t I generate more today?”

That shift in conversation revealed something deeper than engagement.

It revealed dependency.

And dependency is often the first real sign of product-market fit.

The mistake most founders make: building a cool demo instead of a business

At some point, Yan breaks down the core mistake he sees everywhere in AI startups.

It’s simple, but brutal:

Most founders fall in love with demos.

A demo feels like progress because it looks impressive. It showcases capability. It gets reactions.

But a demo is not a business.

A business only exists when one condition is met:

People are willing to pay because their real problem is being solved.

And real problems have a specific structure:

→ They are already being solved manually → They are time-consuming or expensive → People are stitching together messy workflows to get it done

If a problem is truly painful, there is always a workaround already in place.

That’s the real opportunity.

Not inventing new behavior—but replacing existing friction.

When you know it’s real: the credit card moment

One of the strongest signals isn’t excitement.

It’s willingness to pay.

If users say:

“This is amazing!”

But never ask about pricing…

You don’t have a business yet.

But when users start asking:

→ What’s your pricing? → What are the tiers? → Can I upgrade?

That’s when things change.

Because now the product is no longer a “nice tool.”

It is something they are integrating into their workflow.

Passion vs problem: the misunderstood equation

A lot of founders think passion means loving the specific problem they’re solving.

Yan disagrees.

Passion, in his view, is not about video editing or SaaS or AI.

It’s something deeper:

→ The passion to be a builder → The passion to solve problems → The desire to create something people actually use

That’s the emotional layer.

But emotion alone is not enough.

The rational layer is equally important:

→ Deep understanding of the industry → Knowledge of workflows and user behavior → Awareness of what people are already doing manually → Clarity on what pain is truly unavoidable

One without the other breaks the system.

Emotion without understanding creates fantasy.

Understanding without emotion creates hesitation.

Founders need both.

From tool to system: the rise of “Agent Opus”

As the product evolved, Opus Clip expanded beyond simple clipping tools into something more complex: agent-based creation systems.

The idea behind “Agent Opus” is a shift in architecture.

Instead of a fixed workflow, users can simply provide input:

→ A link → A story → A piece of content → Even a raw idea

And the system decides everything else.

But the key shift is conceptual.

This isn’t a video editor anymore.

It’s not even a content generator.

It behaves more like a director.

A film director doesn’t personally edit every frame or design every scene. Instead, they coordinate specialists:

→ Writers → Designers → Producers → Editors → Researchers

Agent Opus mirrors that structure with multiple sub-agents working under a central “director” agent.

The result is a fully orchestrated workflow:

→ Script creation → Voiceovers → Visual generation → Scene selection → Infographic insertion → Final video assembly

What emerges is not a tool.

It’s a system that produces content end-to-end.

Turning a LinkedIn post into a viral video

One of the most striking demonstrations of this system is simple.

A user can take something like a LinkedIn post—even a casual one—and turn it into a full video.

For example, a post about working from a messy garage during a chaotic home day, which already had over 120,000 impressions, can be transformed into a structured video narrative.

The process is straightforward:

→ Paste the link → Add optional prompts (like “make it viral”) → Choose voice style → Let the system generate structure

Then the system builds:

→ Hook templates → Script → Visual sequence → Voiceover → Edited output

The only tradeoff today is time. It can take 30–60 minutes to render, though optimization is ongoing.

But the direction is clear: content repurposing is becoming automated at the system level, not the tool level.

What happens to creators in the next 3 years

As tools like Opus Clip evolve, the creator economy is shifting underneath them.

The biggest change is this:

The barrier to creation is collapsing.

That means:

→ Anyone can create content → Anyone can publish across platforms → Anyone can generate visuals, scripts, and edits

Which leads to a paradox.

Creation becomes easier, but competition becomes harder.

Because when everyone can create, the differentiator is no longer execution.

It becomes identity.

Creators will no longer win by editing better videos.

They will win by having:

→ A unique perspective → A recognizable voice → A clear narrative → A consistent point of view

In other words, AI handles the production.

Humans handle the meaning.

The saturation fear: will attention run out?

A natural question emerges from this shift:

If everyone can create, won’t content become overwhelming?

Won’t attention become the real scarcity?

Yan’s perspective is more grounded.

He compares it to transportation.

A century ago, having a car was rare. Speed was a privilege.

Today, almost everyone can drive.

But that didn’t eliminate elite performance.

It created Formula 1.

Everyone gained access to the baseline.

But excellence still separated the top 0.1%.

Content will behave the same way.

The baseline rises. The ceiling rises with it.

And the gap between average and exceptional becomes even more visible.

The real lesson hidden in all of this

The story of Opus Clip is not really about AI video tools.

It’s about something more fundamental:

A startup is not validated when it looks impressive.

It is validated when it replaces something painful people are already doing.

Everything else is noise.

Demos can lie.

Metrics can mislead.

But behavior doesn’t.

When users come back repeatedly, complain about limits, and start asking for pricing without being pushed…

That’s the moment the illusion breaks.

And something real begins to take its place.

[talhaaiautomation]

I Turned Claude Into a 24/7 Trader

A week ago I did something that started as a simple experiment: I gave an AI trading agent $10,000 in an Alpaca account and let it manage the portfolio for 30 days.

What surprised me wasn't that it made trades. It was that it actually beat the S&P 500 by roughly 8% during that period.

At the time, I was running the system through an Open Claude agent setup. Then Anthropic released Claude Opus 4.7, and while looking through the benchmarks, one thing caught my eye: its financial analysis performance improved noticeably. That got me wondering whether I could take what I'd learned from the first experiment and build something more reliable that runs completely inside Claude Code.

So that's what I spent time building: a 24/7 AI trading agent powered by Claude Opus 4.7 and Claude Code Routines.

I'm not talking about day trading or trying to build a money-printing machine. The goal is much simpler: create an autonomous system that researches companies, manages positions, documents its reasoning, and tries to outperform the S&P over the long run.

What I wanted the agent to do

The idea was to have the agent operate throughout the trading week without me manually triggering anything.

The system needed to:

➜ Research market news and company fundamentals ➜ Monitor existing positions ➜ Place trades through Alpaca ➜ Keep a journal of decisions and lessons learned ➜ Generate reports and notifications ➜ Maintain memory between runs ➜ Operate on a schedule even when my computer is off

For the stack, I used:

➜ Claude Code Routines ➜ Claude Opus 4.7 ➜ Alpaca API for brokerage ➜ Perplexity API for research ➜ ClickUp for notifications ➜ GitHub for persistence ➜ Claude Desktop + VS Code

A lot of those pieces could be swapped out, but I wanted to see how far I could push this exact setup.

One thing I realized pretty quickly is that the trading strategy itself isn't actually the hardest part.

The bigger challenge is memory.

Every time a routine runs, Claude essentially wakes up with no memory of previous sessions. If you want the agent to behave consistently, learn from mistakes, and remember what happened yesterday, you have to design that memory layer yourself.

The approach I settled on was simple.

Every routine:

➜ Reads a set of memory files ➜ Understands the current state of the portfolio ➜ Performs its assigned task ➜ Writes back anything important for future runs

That means the next routine wakes up, reads those files, and immediately has context.

Instead of treating files as storage, I started thinking of them as the agent's memory and personality.

Before building anything, I also spent time thinking about context management.

Even though modern models have massive context windows, I don't like assuming I can throw unlimited information at them. Every file, trade log, research note, and instruction consumes context.

I treated tokens like a budget.

The cleaner the memory structure, the more efficiently the agent can operate.

That ended up influencing almost every design decision.

For the actual trading strategy, I migrated a lot of knowledge from my previous Open Claude trading agent.

I basically asked it to export everything:

➜ Trading rules ➜ Buy signals ➜ Sell signals ➜ Research process ➜ Portfolio history ➜ Lessons learned ➜ Trade logs ➜ Review documents

The old agent had already spent 30 days researching and operating, so it seemed wasteful to start from scratch.

Instead of retraining a new agent, I wanted to transfer everything it had already learned.

I had it generate exports of its files, then dropped those directly into the new Claude Code project.

Once those files were in place, I opened Claude Code and told it:

You're becoming the new version of this trading system. Learn everything from these files, understand the strategy, and reorganize the project in the way that makes the most sense for you.

That worked surprisingly well.

Claude reorganized the project structure, created memory folders, built command systems, and proposed a cleaner architecture than what I originally had.

Something interesting happened during this migration.

Claude noticed that one of the files contained a live Alpaca API key.

That was a good reminder that when you're moving projects around, it's easy to accidentally expose credentials.

I immediately rotated the keys and moved everything into environment variables instead.

That ended up becoming part of the project design:

➜ No secrets stored in GitHub ➜ All API credentials loaded from environment variables ➜ Remote routines only access secrets through the environment

Another thing I spent a lot of time on was guardrails.

Autonomous systems are eager.

If you don't define limits, they'll happily keep making decisions.

So before allowing real trading, I started thinking about rules like:

➜ Paper trade first ➜ Position size limits ➜ Daily loss limits ➜ Maximum number of new positions ➜ No options trading ➜ Risk management requirements

My view is that you shouldn't throw an agent into a live brokerage account and hope for the best.

You treat it the same way you'd teach a kid to ride a bike.

You start slow.

You supervise.

You add training wheels.

Then you gradually increase autonomy.

Once the project structure was stable, I moved on to routines.

This was probably the coolest part of the build.

Instead of one giant workflow, I broke the trading day into separate routines.

Claude analyzed the strategy and proposed a schedule:

Pre-market

➜ Research catalysts ➜ Review news ➜ Generate trade ideas

Market open

➜ Execute approved trades ➜ Set trailing stops ➜ Update trade logs

Midday

➜ Monitor positions ➜ Cut losing trades ➜ Tighten stops on winners

Market close

➜ Review portfolio performance ➜ Update memory files

Friday review

➜ Weekly performance analysis ➜ Compare against benchmarks ➜ Grade decision quality

Each routine had its own prompt, responsibilities, and memory workflow.

At that point I ran into another challenge.

Local routines are easy, but they only run while your machine is active.

I wanted the system running even if my laptop was off.

The solution was using remote routines.

That meant pushing everything to GitHub and letting Claude spin up cloud environments whenever a routine executes.

The workflow looks something like this:

➜ Routine starts ➜ GitHub repo is cloned ➜ Memory files are loaded ➜ Research and trading tasks run ➜ Updates are written back ➜ Changes are committed to GitHub ➜ Environment is destroyed

The next routine then starts from the updated repository state.

That solved the persistence problem.

Getting the routines running wasn't completely smooth.

During testing, the first execution failed because it couldn't find the Alpaca credentials.

The issue turned out to be simple.

The environment variable names didn't exactly match what the prompts expected.

Once I fixed the naming mismatch, everything started working correctly.

That's why I strongly recommend manually triggering every routine several times before trusting the schedule.

You'll catch issues immediately.

After fixing that, I ran a full test.

The weekly review routine successfully:

➜ Connected to Alpaca ➜ Read portfolio data ➜ Generated a performance report ➜ Updated memory files ➜ Committed changes back to GitHub ➜ Sent a notification through ClickUp

I checked the account balance reported by Claude and compared it with Alpaca.

The numbers matched perfectly.

That was the moment where the whole system finally felt real.

Right now the setup runs:

➜ 6:00 AM ➜ 8:30 AM ➜ Noon ➜ 3:00 PM ➜ Friday weekly review

And every run updates its own memory so the next run understands what's happening.

The project is still evolving. I'm constantly reviewing logs, refining prompts, improving skills, and adjusting guardrails. I'm not claiming this is some magical trading system, and this definitely isn't financial advice. For me, the interesting part wasn't even the trading. The interesting part was realizing that if you design memory correctly, Claude Code Routines can function like a long-running autonomous worker that maintains discipline over time. The biggest lesson from this project is that the files aren't just storage. They're the agent's memory, decision-making framework, and personality. Once I started thinking about it that way, the entire architecture made a lot more sense.

Watch my videos on youtube - @talhaaiautomation1

Read more article on medium - Talha | Ai Automation – Medium