Key Takeaways

• I earned £1,500 within 48 hours of deploying my AI agent system with a £10 million revenue target
• The “best” AI agent is the one that knows your business, not the most popular tool
• Hermes, Open Cloud, and Open Router offer powerful free alternatives to expensive subscriptions
• Installing too many skills wastes API tokens and crashes your system—strategic selection matters
• My curated database covers 250+ skills for 2026, with GitHub links and purpose ratings

Why Most AI Automations Never Make Money

Here’s the honest question I ask everyone: how many of you have actually earned money from the automations you’ve built? Most people haven’t. The reason is technical but simple—these systems are built to look impressive, not to function profitably.

When I started, I fell into the same trap. I’d install dozens of skills, connect multiple APIs, and create what I thought was a “Jarvis” system I could talk to. It felt powerful. But every conversation burned through thousands of tokens. Two questions in, my API limit hit, and I’d wait an hour to continue. The system was technically correct but practically useless for real business operations.

The breakthrough came when I realized that memory management is everything. Every skill you add requires the AI to read and process it. More skills = more tokens = higher costs and slower performance. I was essentially paying to make my own system worse.

How I Built My “Dream Team” of AI Agents

I stopped trying to find the “best” agent and started building agents that knew me. Here’s what that looks like in practice:

I created specialized agents based on real business minds. My Elon Musk agent thinks in first-principles and challenges my assumptions about scaling. My Warren Buffett agent analyzes my investment decisions and crypto trades. My Gary Vaynerchuk agent pushes my content strategy. They’re not perfect copies—obviously—but they embed different perspectives into my decision-making.

The result? I’m making better decisions faster because I’m stress-testing ideas against multiple mental models automatically. When I wake up, the system has already researched opportunities, identified risks, and prepared recommendations aligned with my goals.

The £1,500 Breakdown: What Actually Worked

I’m transparent about this: the £1,500 came from specific e-commerce activities my agents handled end-to-end. My eBay agent lists products, responds to customer messages, optimizes visibility, and manages advertising. When I told it to “create a design and publish it on Amazon KDP” and “create a 30-day fitness plan PDF for Etsy,” it executed while I was eating dinner.

The sales came in. The system auto-delivered to customers. I was at my day job while the automation earned.

But I need to be direct: this requires correct setup, ongoing refinement, and skill. It’s not magic. The people who fail treat AI agents like plug-and-play money machines. The people who succeed treat them like employees that need training, clear instructions, and proper tools.

My Curated Tool Stack for 2026

I’ve compiled nearly everything available—Hermes, OpenClaw, CloudSkill, Cursor, Codex, Gemini, GitHub integrations, Windsurf, OpenCode, Framework—into a single database. Not because you should use all of them, but so you know which ones to ignore.

Here’s my current assessment:

• Hermes: My top recommendation for beginners. Open-source, runs locally, 23,000+ community members. I use it for my eBay store, Telegram, Discord, WhatsApp, and Signal integrations.
• Open Cloud: Extremely advanced skill ecosystem. I use it for crypto analysis and content automation. Warning: significant security considerations—use a sandbox server.
• Cursor: 30,000+ skills available. Best for coding workflows.
• Codex: 10,000+ skills. Strong for debugging and code generation.

The key insight: Hermes and Open Cloud have overlapping capabilities. I started with Hermes (free, local), then migrated to Open Cloud when I needed more power. The migration takes seconds with a single command.

How to Start Without Spending Money

If you have a powerful computer, download Ollama and run models locally with unlimited free API usage (within their terms). The Q1 2.5 Codex Coder model runs at 4-5 GB and works offline—I used it on a plane with no internet.

If your computer struggles, use Open Router. Filter for “Free” models. Nvidia currently offers excellent free API servers. Check the Leaderboard to match models to your specific task—Q1 3.6 wins on cost-efficiency for language tasks, HY3 leads for programming, Gemma 2.5 Flash dominates content creation (though it’s paid).

For installation without coding knowledge, I recommend Antigravity or VS Code. Copy the install link, paste it in, and your agent configures in seconds. I demonstrated this live—my video content agent was running immediately after clicking “install.”

The Skills That Actually Matter

Think of skills like The Matrix—Neo downloads kung fu and instantly knows it. AI skills work the same way. But you wouldn’t download “helicopter piloting” if you’re learning self-defense.

I categorize essential skills by purpose:

• Research: Deep-dive analysis and market intelligence
• Content: Creation, editing, multi-platform distribution
• Developer: Code writing, debugging, API integration
• Productivity: Workflow automation, scheduling, task management
• Multi-Agent: Multiple specialized agents communicating and self-improving without constant human prompting

My rating system prioritizes daily utility. A “7” for me might be a “10” for you if you’re in prompt engineering or platform-specific development. The GitHub links are all verified and directly accessible from my database.

Critical Security Warnings

I must say this clearly: do not install everything you find. Each installation consumes disk space and creates potential vulnerabilities. Anthropic’s cybersecurity skills are powerful—intended for protecting systems, not attacking others. “Uncontrolled power is not power.” Use sandbox servers (I recommend CloudFix AI or Bluehost’s AI-optimized systems) so your projects continue even when your computer is off.

Verify sources rigorously. Official tools from Anthropic, Google, and established companies are safest. Community skills are valuable but carry higher risk—evaluate before installing.

FAQ

What’s the cheapest way to start with AI agents?

Download Ollama for local running with free API limits, or use Open Router’s free tier. Hermes through Antigravity requires zero upfront cost. I ran my first profitable system on entirely free infrastructure before scaling to paid APIs for better results.

How is your agent different from ChatGPT or Claude?

My agents are specialized, persistent, and integrated into my business systems. They don’t just answer questions—they execute tasks across eBay, Etsy, Amazon KDP, Telegram, and more. They remember context across sessions and improve based on outcomes, not just conversation history.

Do I need coding skills to set this up?

Not necessarily. Tools like Antigravity provide one-click installs. However, understanding basic API concepts helps significantly. I provide step-by-step walkthroughs in my School community livestreams, and the setup takes minutes rather than hours with proper guidance.

Why did you only make £1,500 if the target is £10 million?

£1,500 in two days from a new system validates the approach—it doesn’t complete the journey. I’m sharing real early results, not final outcomes. Scaling requires iterative improvement, additional skill integration, and market expansion. The system is working; optimization continues.

Final Thoughts

AI agents have reached a different dimension of capability. What I can do now—waking up to researched opportunities, running businesses through voice commands, automating entire sales cycles—still surprises me. But the tools are only as good as your strategy for using them.

Start with Hermes if you’re new. Migrate to Open Cloud when you need more power. Never install skills blindly. Always prioritize security. And most importantly, know what you’re building toward before you build it—because the most impressive-looking system is worthless if it doesn’t serve your actual business goals.

What will you use this for? Which agent are you starting with? I genuinely want to know—share in the comments below.

Watch the full video (in Turkish — English subtitles available):

Tools & Community

• TurkoLister — the AI listing tool I use to turn Amazon products into optimized eBay UK listings in about 60 seconds (from £4.99/month, £1 one-week trial).
• AI & E-commerce Community — my Turkish-speaking community ($19/month) with weekly live sessions.
• Subscribe on YouTube — new experiments every week.

The post I Built an AI Agent System That Made £1,500 in 2 Days appeared first on Digital Market Mentoring.

Key Takeaways

• Use AI to reverse-engineer a 10-year roadmap by modeling people already living your desired life
• Read one hour daily in your field—this compounds to roughly 10 years of accelerated progress
• Your subconscious belief about money determines your ceiling more than your skills do
• The UK market rewards problem-solving software with acquisition offers, not just subscription revenue
• Act from calm confidence, not panic—opportunities arrive when your energy attracts them

Step 1: I Used Claude to Reverse-Engineer My 10-Year Roadmap

Here’s a prompt I now give every entrepreneur I mentor. Open Claude, ChatGPT, or Gemini and write something like this:

“I enjoy [X], I’m [Y] years old, and my life purpose is [Z]. Who in the world is currently living where I want to be in 10 years? Give me 5 key steps they took, as if they were my life coach.”

What happens next is powerful. The AI doesn’t just give generic advice—it maps a specific path. In my case, it might say: “You enjoy content creation and helping people. Your best path is building software that facilitates social connection. Someone doing this successfully for 20 years started by seeking investors rather than working for others first, then built email campaigns and reached out via social media. One person invested £10 million. That’s how they reached where they are.”

Is it really this simple? I used to think “life hacking” was exaggerated. But the truth is: your life isn’t long enough to solve everything through trial and error. This prompt gives you a roadmap. I’ve shared the original version in my School community (link below), but you can build your own with that framework.

Step 2: I Read One Hour Daily—And It Compressed a Decade of Learning

The biggest mistake I see now? Outsourcing all thinking to AI. Yes, I run an AI and automation blog. But I still read physical books—one hour every day.

Here’s why: a single book often contains 30-50 years of someone’s distilled experience. The more lives you experience through reading, the better your decisions become. I’ve seen people who read daily in their field advance what feels like 10 years ahead of peers who don’t.

Most people say they don’t have time. Yet they spend three hours on social media watching random videos. This is a choice. If you’re not happy with your results, something needs to change.

Critical caveat: Don’t become dependent on any single book. The author gives you their formula—X led to Y profit in Z time. Understand the logic, but adapt it. If they made money from coffee shops and you want to open a restaurant, extract the principle, not the exact tactic. The formula must be customized to your direction.

Step 3: I Had to Rewire My Brain to Believe Money Was Achievable

This was the hardest lesson. Years ago, I was making significant daily income—actual money hitting my bank account, paying for meals, covering expenses—and I still couldn’t believe it was real. I’d think: “Money this easy can’t be possible.”

I was wrong about “easy.” I don’t mean get-rich-quick schemes. I mean: people who understand the value of their knowledge earn more. I give consulting at what some might consider high rates—£5,000 for strategic guidance. Why? Because I know the roadmap I provide will earn my clients far more than my fee. When I was earning over £10 million monthly, I knew my consulting fee was conservative.

Here’s the psychological shift: once you earn £10,000 and see it in your account, your brain starts asking “Why not £100,000?” The taboos break. Your brain self-educates. You’ll wonder why you spent years on other approaches.

But this only works from a state of calm and security—not panic. When you’re desperate, you repel opportunities. When you’re centered, the universe (or let’s say: your pattern-recognition) starts showing you videos, books, and connections that were always there but invisible to anxious eyes.

Know your value. A gold seller doesn’t negotiate at the bazaar. They know the price. If you know your worth, you don’t need to bargain—opportunities find you.

Step 4: I Built Software to Solve One Problem—Then Sold the Company

The UK, in my experience, is one of the world’s best environments for this strategy. The mindset here is different. People doing million-dollar deals don’t obsess over minor details. They think: “This person built something. If I buy it for £5 million, I can sell for £25 million.” They’re aware of value.

Here’s the exact playbook I see working with young UK entrepreneurs I mentor (and use myself):

1. Find one problem people have
2. Study existing solutions and identify their gaps
3. Build a simple system—using Claude, AI coding tools, or no-code platforms
4. Share it on social media, do PR, offer it free
5. Collect feedback from 50-100 users
6. Iterate based on real usage
7. List on Flippa or Empire Flippers

I started my own software free this way. The world’s biggest companies—Autopilot, OpenAI, Google, major AI firms—acquire these businesses. Why? For the data. For the customer base. They buy the users.

Manus AI is a perfect example. I made one of the first videos about it, reached hundreds of thousands, other creators shared it—and Facebook acquired it for millions. The formula: provide value first, let purchase follow naturally. Every major startup operates this way now. Don’t sell small subscriptions to small customers. Optimize, then play for the big acquisition.

That software you might sell for £20/month? A company will buy for cash—serious cash—and put it in your pocket.

Step 5: I Learned to Treat Life as a Journey, Not a Sprint

Life goes up and down. What matters is your reaction. Two people face identical situations; one thrives, one stalls. The difference? Perspective.

Your brain can be trained. Hard times pass. As long as you don’t lose internal belief, things align with that belief. Ask yourself: if today were your last, what would you do right now? Start doing those things—life gains meaning when you do.

Here’s what I observe: people working 10-hour days aren’t all billionaires. The wealthiest often work the least. Why? They spend 30 hours thinking through what problem to solve, then solve it once and sell that solution forever. The other person just works more hours.

Act as if you’ve already achieved what you want. It arrives faster that way. Otherwise, the math doesn’t work—everyone grinding 10 hours daily would be rich. They’re not.

FAQ: Common Questions About Building AI-Powered Businesses

Which AI tool works best for the 10-year roadmap prompt?

I’ve tested this across Claude, ChatGPT, and Gemini. The results are similar if your prompt is specific. The key is including your genuine interests, age, and stated life purpose—generic inputs produce generic outputs.

How much can I realistically sell a micro-SaaS for in the UK?

Based on what I’ve observed on Flippa and Empire Flippers, problem-solving software with verified user data and feedback history typically lists from low six figures to several million pounds. The acquisition price depends on data quality, user count, and strategic fit for buyers—not just revenue.

Do I need coding skills to build software for acquisition?

No. I use Claude and AI coding tools to build systems. The critical skill is identifying a genuine problem and validating it with real users—not writing code yourself. Many successful exits come from founders who focused on problem-validation and user acquisition.

Why give software away free instead of charging £50-100/month?

Because user data and verified feedback are worth more to strategic acquirers than subscription revenue. Fifty free users providing detailed feedback creates an asset that platforms like OpenAI or Google will pay to acquire. Immediate revenue often caps your ceiling.

Conclusion

These five steps aren’t theoretical. I’ve lived them—from disbelieving my own success, to reading my way ahead, to building and exiting software. The tools are here. The UK market is unusually favorable for this approach. What remains is your belief and your willingness to start.

Freedom is the goal. Money is a tool. Everything else follows. If you can achieve that freedom, I’m genuinely happy for you. Share your own experiences in the comments—I’d love to hear what’s working for you.

Watch the full video (in Turkish — English subtitles available):

Tools & Community

• TurkoLister — the AI listing tool I use to turn Amazon products into optimized eBay UK listings in about 60 seconds (from £4.99/month, £1 one-week trial).
• AI & E-commerce Community — my Turkish-speaking community ($19/month) with weekly live sessions.
• Subscribe on YouTube — new experiments every week.

The post 5 AI-Powered Steps That Changed How I Build Businesses appeared first on Digital Market Mentoring.

Key Takeaways

• Anthropic’s unreleased “Mythos” series model Cloud Oceanus V1 leaked from developer console on June 3, 2026—before any official announcement
• Rumored specs: 1 million token context window and 52 tokens per second processing speed
• Appeared on black market within hours at $16 per million input tokens, $80 per million output tokens—well above standard enterprise pricing
• Anthropic allegedly halted all red team access and launched internal investigation
• None of this officially confirmed by Anthropic—all information based on leaks and security sources

How the Oceanus Leak Unfolded

On June 3, 2026, researchers spotted Cloud Oceanus V1 appearing in Anthropic’s own developer console. This wasn’t a routine update. The model belongs to Anthropic’s “Mythos” series—a tier they had already flagged as potentially dangerous when earlier models leaked in April 2026.

Here’s what makes this different from typical AI news: Oceanus reportedly represents the level above those already-concerning models. Anthropic’s own security team had previously published findings showing that even their existing Claude models could identify previously unknown software vulnerabilities—zero-days—without special tools. They put this on their official blog. Oceanus, by all leaked accounts, pushes that capability further.

Within hours of red team access opening, the model appeared for resale through Chinese proxy servers. The pricing structure was telling: input at $16 per million tokens, output at $80 per million. For context, that’s substantially above normal Anthropic enterprise rates. The most dangerous model in their lineup hit the black market before formal testing even began.

Anthropic’s response, according to leaks: immediate shutdown of all red team access and an internal investigation. “Everyone step back.”

Why Anthropic Is Keeping This Model Hidden

This is where I get genuinely concerned as someone building businesses on AI infrastructure. Anthropic has essentially said: we have technology too powerful to release safely. The company that built the model doesn’t trust the safeguards available across the industry.

Think about that. We’re not talking about science fiction. A few years ago, this would have been a movie plot. Now it’s Tuesday in the AI industry.

The specific fear? Capabilities that outpace misuse prevention. If current models can find zero-days in well-tested software, and Oceanus sits a tier above, the attack surface expands dramatically. Anthropic’s stated position: they won’t release until security measures exist that the entire sector currently lacks.

I want to be crystal clear: Anthropic has not confirmed any of these details on their official channels. Multiple security sources report consistent information, but we’re operating on leaks and circumstantial evidence. The console sightings appear documented, but the full capability claims remain unverified.

What This Leak Reveals About the AI Race

Three patterns emerge from this incident that I think every online business owner should understand:

First: speed of exposure. Models are leaking before they launch. The competitive pressure, internal access complexity, and sheer value of frontier AI means secrecy is becoming nearly impossible to maintain. For those of us building on these platforms, this creates volatility. Today’s exclusive capability becomes tomorrow’s commodity—or yesterday’s black market product.

Second: capability-danger correlation. The more powerful these systems become, the more restricted their release. We’re entering a phase where the most capable tools may not be publicly available at all. This bifurcates the market: general-purpose AI for consumers, gated capabilities for vetted organizations, and leaked versions circulating unpredictably.

Third: the naming coincidence. I’ll admit personal interest here. Oceanus in Greek mythology rules the ocean. My company Okyanusum means “my ocean”—a name I chose years ago to represent dreams as vast as the sea. Anthropic’s “Mythos” series pairing with my “ocean” concept? Probably coincidence. Probably. But it reminded me why I built Okyanusum in the first place: consolidating fragmented AI tools into unified workflows.

The Real Business Lesson: Focus on Value, Not Hype

Watching this unfold, I keep returning to something I tell people in my training sessions. AI capabilities will keep exploding. Oceanus today, something else tomorrow. The question isn’t which model drops when—it’s whether you’re building something valuable with whatever you have access to now.

I’ve been in online commerce for 21-22 years. I wrote a book documenting my path from starting out to building significant revenue over two decades. The pattern I see: people who succeed aren’t those chasing every new tool. They’re those who solve real problems for real people.

Here’s a concrete example from my own operation. I identified a problem: businesses paying £200-300 monthly for scattered software tools. My solution: one platform consolidating those functions at roughly £20. The value proposition writes itself—same capabilities, 10x cost reduction, unified interface.

Reaching potential customers? I use AI-powered outreach specifying exactly what I offer, what they currently pay, and what changes. “You’re spending £300 on separate tools. I built one system at £20 that handles everything. Interested?” When properly configured, AI handles the volume while I handle the relationships.

Practical Steps for AI-Powered Business Growth

For those still navigating how to actually apply AI in business—because I know 80% of the world barely understands what’s happening—I have specific recommendations:

Start with foundational education. Anthropic offers free training materials on their official site. The videos include translation tools. Even without English fluency, you can follow along. These resources explain sub-agents, cloud co-working, and agent capabilities more clearly than most paid courses I’ve encountered.

Think geographically expansive. I work from London, but my perspective isn’t limited to my neighborhood. When I started, I could see only my immediate surroundings—maybe Bursa, maybe my local area. The internet changed that. Your potential market isn’t the shop next door; it’s the connected world. AI accelerates that reach.

Build systems, not one-off tactics. I’ve developed live implementation programs across eBay dropshipping, Facebook dropshipping, Etsy, Amazon, Shopify, and additional marketplaces. Not recorded courses—live builds where participants ask questions in real-time. Over 400 people across different countries have gone through this. Why live? Because static information becomes outdated; interactive problem-solving creates lasting capability.

The core principle: AI tools amplify value you’ve already identified. They don’t create business models from nothing. If you understand a customer’s pain point—scattered software, slow processes, high costs—AI lets you address it faster and at greater scale.

FAQ

What is Anthropic Oceanus?

Oceanus (reportedly “Cloud Oceanus V1”) is an unreleased AI model from Anthropic’s “Mythos” series, discovered in their developer console on June 3, 2026. It allegedly features a 1 million token context window and 52 tokens per second speed. Anthropic has not officially confirmed its existence or capabilities.

Why is Oceanus considered dangerous?

Based on leaked information and Anthropic’s own prior research, frontier models in this class may identify previously unknown software vulnerabilities (zero-days) without specialized tools. Anthropic’s security team published findings about existing models demonstrating this capability; Oceanus reportedly extends it further.

How much did leaked Oceanus access cost?

According to leak reports, black-market pricing was $16 per million input tokens and $80 per million output tokens—substantially above standard Anthropic enterprise rates. The model allegedly appeared for resale within hours of red team access opening.

Has Anthropic confirmed the Oceanus leak?

No. As of the information available, Anthropic has not officially confirmed Oceanus, the leak, or related security measures on their website or official channels. All reported details come from security sources, researcher observations, and unverified leaks.

Conclusion

The Oceanus leak—confirmed or not—illustrates where AI is heading. Capabilities are advancing faster than our frameworks for safe deployment. For business builders, this means opportunity and uncertainty in equal measure.

I don’t believe in chasing every model release. I believe in understanding tools deeply enough to solve real problems. Whether Anthropic officially launches Oceanus tomorrow or buries it entirely, my focus stays constant: how do I use available AI to deliver more value to more people, more efficiently?

That mindset—problem first, tool second, scale third—is what I’ve tried to build into Okyanusum and everything I teach. The ocean of possibility is vast. The ones who navigate it successfully aren’t those with the newest map. They’re those who learn to read the currents.

Watch the full video (in Turkish — English subtitles available):

Tools & Community

• TurkoLister — the AI listing tool I use to turn Amazon products into optimized eBay UK listings in about 60 seconds (from £4.99/month, £1 one-week trial).
• AI & E-commerce Community — my Turkish-speaking community ($19/month) with weekly live sessions.
• Subscribe on YouTube — new experiments every week.

The post Anthropic’s Secret Oceanus AI Leaked: What It Means for Business appeared first on Digital Market Mentoring.

In this guide, I’m going to walk you through exactly how I built Jarvis, what components you need, what it costs, and the critical mistakes most people make that cause their systems to fail. No coding background required.

Key Takeaways

• Jarvis runs on 4 core components: a brain (LLM), voice (11 Labs), ears (Whisper), and hands (Cloud code)
• I use Minimax as the brain with their API—currently at 7% discount, costing me a fraction of what I’d pay with other providers
• 6 million tokens processed so far; without this cost-efficient setup, I’d be paying thousands of pounds monthly
• Voice cloning requires just 2 minutes of quality microphone recording in 11 Labs
• The most common failure point: connecting APIs without authorizing the actual social media accounts (Blotato example)
• My system replaces what would cost ~£20,000/month in human staff across content, sales, and e-commerce operations

The Four Body Parts of Jarvis

When I break down how Jarvis works, I think of it as building a person. You need four things: a brain, a voice, ears, and hands. Combine these through Cloud code, and you have an AI assistant that can execute tasks on your computer both locally and remotely.

The beauty of this approach is that instead of writing commands and waiting seconds for responses, everything runs blazingly fast in the background. I’ve tested dozens of configurations over the past two years, and this architecture is what actually delivers production-level results.

Step 1: The Brain — Cloud + Minimax API

The foundation of everything is Cloud. I download the Cloud code to my computer, and this becomes the orchestration layer where all components talk to each other. Think of it as Jarvis’s nervous system.

For the actual brain—the LLM processing all decisions and responses—I chose Minimax. Here’s my honest reasoning: I’ve tested hundreds of APIs and open-source models, and Minimax hits the sweet spot of speed, intelligence, and cost that I need for serious business operations.

Right now, Minimax M3 is running at a 7% discount, which makes it even more attractive. But I want to be transparent: you can use free open-source LLMs if budget is tight. I don’t recommend it for mission-critical work, but the option exists.

To get started, you create a new API key in your Minimax dashboard, name it whatever you want, and save that key for the next step. I’ve processed 6 million tokens through this setup so far, and I can see exactly what each API costs me. Without this visibility and efficiency, running a 24/7 AI system would burn through thousands of pounds monthly.

Step 2: The Voice — 11 Labs Voice Cloning

This is where Jarvis gets personality. I wanted my assistant to speak with my own voice, and 11 Labs makes this possible with just 2 minutes of recording.

Here’s what you do: go to 11 Labs, navigate to “My Voice,” click “Create Voice,” and either record directly or upload a clean audio file of yourself speaking. I strongly recommend using a proper microphone—the difference in output quality is dramatic.

Let me show you how far this technology has come. I have a voice clone I created nearly two years ago. Listen to how robotic it sounds: “Akın Yılmaz’ın sesli komuta asistanıyım.” Flat, lifeless, clearly artificial.

Now compare that to my current clone. I literally can’t tell the difference sometimes. The quote “Başarı hakkında hayal kurmak sizi başarıya götürmez. Ama çalışmak götürür”—I recorded that, and the clone delivers it with nuance that matches my actual speaking style.

For those who don’t want to use their own voice, 11 Labs offers free stock voices with generous limits. I use these for client-facing agents—like automated appointment booking calls—where my personal voice isn’t necessary. But for my own command center? My voice, my preference.

Once cloned, copy your Voice ID. You’ll paste this into the Cloud configuration so Jarvis knows which voice to speak with.

Step 3: The Ears — Whisper for Speech-to-Text

Jarvis needs to understand me when I speak. For this, I use Whisper, OpenAI’s open-source speech recognition system that’s completely free.

Whisper does something critical: it converts everything I say into text that the AI brain can process as prompts. When I tell Jarvis “open YouTube” or “analyze yesterday’s sales,” Whisper transcribes that, Cloud routes it to Minimax for processing, and the response comes back through my cloned voice.

The GitHub repositories for all these integrations are documented in detail, and I share the specific links in my community PDF. You copy the Whisper integration code, paste it into your Cloud setup, and you’re three-quarters built.

Step 4: The Hands — Cloud Integration + Clap Listener

Here’s where most tutorials stop and where most people fail. The dashboard running on your local host? That’s just the interface. The real work happens in the backend integrations.

I connect Jarvis to:

• Blotato for automated social media content creation and scheduling
• Twitter/X and LinkedIn APIs for posting and engagement analysis
• eBay APIs for store monitoring and automated product research

But here’s the critical mistake I see constantly: people connect the API but forget to authorize their actual accounts inside Blotato. You can have perfect API credentials, but if your Twitter/X or LinkedIn account isn’t linked and authenticated within the platform itself, the system can’t post or read data. It sees nothing. It does nothing.

I also added a “clap listener”—a fun Python script from an open GitHub repo that puts Jarvis into command mode when I clap. Is it technically necessary? No. Does it feel incredibly cool to clap and have your AI assistant activate? Absolutely. It’s like a doorbell for your digital workforce. You can skip this or implement similar trigger systems by simply asking Cloud to add the functionality.

What Jarvis Actually Does for My Business

Let me be concrete about the value here, because I don’t believe in vague hype.

One of my AI agents scrapes major company websites, finds their API documentation, creates content about their services, and distributes it across social platforms. This generates consistent passive income from content-driven revenue.

Another agent monitors my eBay stores daily—analyzing what’s selling, what’s sitting, identifying similar successful products, and automatically listing new inventory. It delivers a full report to me each morning.

Previously, this level of operation would require approximately £20,000 monthly in human staff salaries. Large companies pay this. My system handles it for what I describe as “almost comically low” running costs.

Jarvis also tracks my progress toward that £1 million target—currently at £123,000—and helps me analyze audience comments to improve my content and identify where people are getting stuck in their own automation journeys.

FAQ: Building Your Own Jarvis

How much does it cost to build Jarvis?

The core setup uses free tools: Cloud (free tier), Whisper (free open-source), and you can start with free LLM options. I pay for Minimax API and 11 Labs voice cloning, but my total operational costs are under £100 monthly despite running 24/7. The exact amount depends on your usage volume and which APIs you choose.

Do I need to know how to code?

No. Cloud handles the orchestration through natural language. You copy integration codes from the repositories I link, paste your API keys, and tell Cloud what to connect. For custom features like the clap listener, you can describe what you want in plain English and Cloud will generate the implementation.

Why did my API integration fail to work?

In 90% of cases I’ve troubleshooted, the API credentials are correct but the actual user accounts aren’t authorized within the third-party platform. For example: connecting Blotato’s API isn’t enough—you must also link and authenticate your Twitter/X and LinkedIn accounts inside Blotato itself. Without this, the system has credentials but no permission to act on your behalf.

Can I use a free voice instead of cloning my own?

Yes. 11 Labs provides free stock voices with usage limits that work perfectly well for most applications. I use free voices for client-facing automation like appointment booking. I only use my cloned voice for my personal command center and specific projects where brand consistency matters. Voice cloning becomes worthwhile if you’re creating significant video or audio content.

Conclusion

Building Jarvis changed how I operate my businesses. It’s not about having a cool gadget—it’s about replacing £20,000 monthly in operational costs with a system that works continuously, learns from my workflows, and scales without proportional cost increases.

The technology I described here—Cloud orchestration, Minimax for reasoning, 11 Labs for voice, Whisper for transcription—is the best combination I’ve found as of today. But I’m constantly testing and upgrading. The moment something better emerges, I’ll integrate it.

If you want to build this yourself, I’ve created a complete PDF guide with all links, codes, and step-by-step instructions available in my community. Drop “carlos” in the comments and I’ll point you to it. And if you get stuck—because you will, everyone does—share your specific error. The difference between people who succeed and those who give up usually comes down to one piece of information they’re missing.

As I tell my community: you earn in this life to the extent of what you know. And you can only level up that knowledge if you invest in yourself.

Watch the full video (in Turkish — English subtitles available):

Tools & Community

• TurkoLister — the AI listing tool I use to turn Amazon products into optimized eBay UK listings in about 60 seconds (from £4.99/month, £1 one-week trial).
• AI & E-commerce Community — my Turkish-speaking community ($19/month) with weekly live sessions.
• Subscribe on YouTube — new experiments every week.

The post I Built a Real-Life Jarvis AI for Under £100 (Full Setup Guide) appeared first on Digital Market Mentoring.

Key Takeaways

• Fugaku uses a “team of experts” approach—multiple top AI models working together through a coordinator, rather than one giant model
• Fugaku Ultra scored 73.7 on industry benchmarks, beating GPT-4.8 (58), Gemini 3.1 Pro (54), and Opus 4.8 (69)
• Solved a Rubik’s cube in 19 steps, fewer than GPT, Gemini, and Claude
• If one model gets restricted or shut down, Fugaku automatically routes to alternatives—you never notice
• Two versions: standard Fugaku for daily tasks, Fugaku Ultra for complex multi-step work
• Pricing ranges from $20/month (standard) to $200/month (maximum tier)
• Not yet available in Europe, UK, or Switzerland as of my testing
• Sakana does not claim to beat Fabio 5—only to be “shoulder to shoulder” with it

Why I Started Paying Attention to Sakana’s Approach

I’ve been watching the AI race for years, and until now, everyone was running the same race. OpenAI, Google, Anthropic, xAI, Mistral—they were all competing on the same metric: who could build the biggest model with the most data and the most compute power. Bigger brain, more power, more parameters. It was a single-genius arms race.

The Japanese team at Sakana asked a completely different question: instead of building one bigger genius, what if we gathered existing geniuses and taught them to work as a team?

That’s Fugaku. From the outside, you ask it one question. Inside, an entire team is working. Each of the world’s best models acts as a specialist in its domain. Fugaku learned on its own which task to assign to which model, when to hand off work, how to combine answers, and how to synthesize the final result. This isn’t a bunch of if-then rules—it’s a trained coordinator. Two scientific papers back this up, so this isn’t a hype claim floating in thin air.

What the Benchmark Numbers Actually Show

I always dig into the numbers before I trust any AI company’s marketing. Here’s what I found when I looked at Fugaku’s performance data:

Fugaku comes in two versions. Standard Fugaku handles daily tasks. Fugaku Ultra tackles complex, multi-step problems. The Ultra version went up against the most powerful publicly available models, and the results caught my attention.

On the key benchmark scores: Fugaku Ultra hit 73.7, while Opus 4.8 scored 69, GPT-4.8 scored 58, and Gemini 3.1 Pro scored 54. That’s a significant gap across the board.

One test that stood out to me: the Rubik’s cube solving benchmark. Fugaku Ultra solved it in 19 steps. GPT, Gemini, and Claude all needed more steps. It found the most efficient path—not by being a bigger single model, but by coordinating specialists effectively.

However, I need to be completely honest here. Sakana does not claim to surpass Fabio 5. Their own wording is “shoulder to shoulder” with it. Since Fabio 5 is shut down, no direct head-to-head testing was even possible. The comparison relies only on extrapolated scores from published benchmarks. Don’t believe videos claiming Fugaku “crushed” Fabio 5—technically, that’s impossible right now.

The Real Innovation: Why No Government Can Shut This Down

This is where Fugaku gets genuinely interesting for anyone building a business on AI. Remember why Fabio 5 mattered so much when it disappeared? One company, one model, gone overnight. Everyone dependent on it was left exposed.

Fugaku solves this structurally. Since multiple models operate inside the system, if one gets restricted, if one company’s API prices spike, if one jurisdiction bans a specific model—Fugaku automatically routes to the alternatives. You don’t notice. The system keeps running and keeps training itself.

Think about what this means: you’re no longer dependent on a single company, a single country, or a single model. This is exactly what I’ve been saying for months—don’t tether yourself to one model. Fugaku has productized that philosophy into a single interface.

Early users in code review report finding over 20 issues where other tools normally catch only 3. The multi-model depth shows up in real workflows.

The Honest Downsides I Found

I promised an honest review, so here are the three concerns that stood out to me:

1. The Hidden Orchestration Layer

Fugaku can obscure which model handles which task, what the actual costs are, and what’s happening under the hood. Transparency matters to me, and this is a genuine trade-off with their approach.

2. Pricing Can Bite

Running that many models isn’t cheap. The Ultra tier especially needs selective use. I saw input costs at $5 and output at $30 for certain operations. The standard plans run $20/month internationally, professional at $100/month, and maximum at $200/month. If you’re not careful about when you invoke Ultra, costs escalate fast.

3. The “Beats Fabio 5” Claim Is Overstated

I’ve seen this narrative spreading online. Sakana themselves don’t make this claim. The reality: Fugaku brings competitive performance to what’s publicly accessible. That’s valuable enough without exaggeration.

Availability and What I Couldn’t Test

I live in the UK, and here’s a frustrating reality: Fugaku isn’t available here yet. The same applies to Europe and Switzerland. Sakana’s site indicates these regions are pending, with a contact form for updates. I translated the Japanese pages to check their hiring process too—they’re recruiting in engineering and business, with document review and interviews as standard steps. If you’re specialized in specific model domains, there may be application paths, though I recommend doing your own research here as I’m working from translated pages.

What This Means for the Future of AI

The bigger picture here matters more than any single product. The question is shifting from “which is the best model?” to “who can combine models best?” Multiple brains working together are producing deeper results than any single model, without the restriction vulnerability.

I believe this direction—distributed, multi-model, self-coordinating systems—is where AI infrastructure is heading. Not because it’s hyped, but because it solves real problems: resilience, specialization, and avoiding single points of failure.

FAQ

What is Sakana Fugaku and how does it work?

Fugaku is a Japanese AI system developed by Sakana that combines multiple leading AI models into a coordinated team. A trained orchestrator assigns tasks to specialist models, manages handoffs between them, and synthesizes their outputs into a unified response. You interact with it as one interface, but multiple models collaborate internally.

How does Fugaku compare to GPT-4, Gemini, and Claude?

On published benchmarks, Fugaku Ultra scored 73.7 versus GPT-4.8’s 58, Gemini 3.1 Pro’s 54, and Opus 4.8’s 69. In practical tests like Rubik’s cube solving, Fugaku Ultra completed it in 19 steps, fewer than competitors. However, Sakana does not claim superiority over the closed Fabio 5 model—only comparable performance based on extrapolated data.

Why can’t governments shut down Fugaku like they did Fabio 5?

Fabio 5 was a single model controlled by one entity. Fugaku distributes across multiple independent models from different providers and jurisdictions. If any single model gets restricted, banned, or priced out, Fugaku’s coordinator automatically routes tasks to available alternatives without user interruption.

How much does Fugaku cost and where is it available?

Standard plans start at $20/month, professional at $100/month, and maximum at $200/month. Ultra-tier usage incurs additional per-request costs ($5 input, $30 output in some cases). As of my research, Fugaku is not yet available in Europe, the UK, or Switzerland—Japan and select other markets have access first.

Conclusion

Sakana’s Fugaku represents a meaningful shift in how we might build AI systems—team-based, resilient, and distributed rather than monolithic and vulnerable. It doesn’t magically surpass closed models that can’t be tested, but it does deliver leading performance among what’s publicly accessible while solving the dependency problem that keeps me awake at night.

The question I’m left with: are you building your business on a single model that could disappear overnight? Or are you thinking like Fugaku—diversified, coordinated, prepared for whatever regulation or market shift comes next?

I’m curious about your take. If you’re exploring how to implement multi-model AI coordination in your own workflows, I’ve linked resources below. Just drop “school” in the comments and I’ll point you toward what I’ve found most useful for getting teams of AI models working together productively.

Watch the full video (in Turkish — English subtitles available):

Tools & Community

• TurkoLister — the AI listing tool I use to turn Amazon products into optimized eBay UK listings in about 60 seconds (from £4.99/month, £1 one-week trial).
• AI & E-commerce Community — my Turkish-speaking community ($19/month) with weekly live sessions.
• Subscribe on YouTube — new experiments every week.

The post Sakana Fugaku Review: Why AI Teams Beat Single Models appeared first on Digital Market Mentoring.