Key Takeaways

• I now have AI agents handling customer messages, competitor research, content creation, and affiliate marketing across eBay, Amazon, Shopify, and Etsy
• One crypto-trading experiment turned £5,000 into £6,000 in a short test — but I pulled out and treat this as experimental only
• Beginners should start with eBay dropshipping or affiliate marketing, not Amazon FBA, which realistically needs £5,000–£15,000 to start properly
• Automation is not optional if you want freedom — a business that stops when you sleep is just another job
• I have been in e-commerce for 21 years; the knowledge you have directly determines the money you make

Why I Disappeared for Two and a Half Months

I stopped posting regularly because I was building something I had postponed for years. At 37, I hit a turning point. I had my AI systems analyze everything I do in a day — necessary tasks, unnecessary tasks, things I enjoy, things I procrastinate on, where I make money, where I waste time. The system organized it all into a structured list. What happened next surprised me: my brain started working differently. With mental space freed up, I could think strategically again.

Here’s what I mean by “differently.” For 21 years — literally the entire lifetime of some people watching my channel — I have worked online. I have worked with some of the most successful people in the world in this field. I felt proud, but I also felt chained. Now AI handles the work 10x to 100x better than I did manually. The question is not whether AI can do the work. The question is: what work are you actually doing, and do you understand how to direct it?

What My AI Agents Actually Do (Specific Examples)

I want to be concrete because I know how frustrating vague “AI automation” claims are. Here are the actual systems running right now:

Customer Communication Agent

When someone messages me privately — “Hi Akin, how are you?” — the system responds naturally. Next day they follow up, it remembers context. When they ask “Should I do Amazon or eBay?” the agent researches current eBay data, compares pros and cons with updated information, and gives a tailored answer. Over time, it builds knowledge on each conversation thread. The consistency and currency of responses are far beyond what I could maintain manually.

If I wanted, this same system could call people using a local phone number, clone my voice, and have a full conversation — analyzing their problems and sending me a summary report. Or it could send voice messages in my voice. Or, using API references of my face and hands, generate a video presentation explaining solutions. This is just the beginning.

Multi-Platform Operations Agent

The system automatically analyzes all my eBay stores, researches competitors in the algorithm, and does the same for Amazon, Shopify, and Etsy. For affiliate marketing, it applies to thousands of affiliate programs, generates video content around those topics, clones the videos, auto-posts to all social media platforms with links underneath, and responds when people ask questions.

Crypto Trading Experiment (With Full Transparency)

I tested a crypto trading system that makes buy-sell decisions in seconds. I put in approximately £5,000 and made around £1,100 profit, withdrawing at roughly £6,000. I stress: this was purely experimental. I have seen YouTubers claim 10x returns, and I wanted to test the mechanics myself. I do not recommend this as a strategy, and I withdrew specifically because I treat it as a learning exercise, not a business model.

The Honest Truth About Starting Out

I need to be direct here because I see too many people lose money. If you know nothing about e-commerce and jump into Amazon FBA with less than £5,000 — realistically £5,000 to £15,000 — you will likely lose it. I am not being pessimistic. I am being realistic about what the process actually requires: product research tools, bulk inventory purchases (often 100–1,000 units), labeling, storage, Amazon storage fees, shipping, PPC advertising, listing optimization, company setup, accounting, taxes. The list keeps going.

People who succeed on Amazon have typically spent 3–5 years learning the system. You cannot compress that into a weekend course and expect the same results. When it does not work, people blame the platform, the course, or anything except the real issue: they tried to skip the learning curve.

Here is how I categorize starting points:

• Beginner: eBay dropshipping or affiliate marketing. Fast payments, low technical barriers, room to learn without catastrophic losses. I have free videos on my channel and School covering these.
• Intermediate: Shopify or Etsy — more control, more complexity, reasonable next steps.
• Advanced: Amazon FBA, your own branded website (WordPress or AI-built on platforms like Lavable), building actual brand equity.

Why Automation Matters More Than Platform Choice

I have managed eBay accounts for others for years, and the pattern is clear. People are smart, eager, and buy courses. But they lack time. They work 7–8 hour jobs, come home to family responsibilities or simply need rest, and cannot dedicate the 3 years of learning, failing, and adjusting that this business realistically takes. Three months to economic freedom? Possible in isolated cases, but not a reliable plan for someone starting from zero.

This is why I automate. A system that only works while you are actively working is not a business — it is self-employment with no boundaries. When you sleep, eat, or take a holiday, income stops. I built my AI agents so that the business operates as if I am a manager receiving a percentage — 10%, 20%, whatever the model yields — while I focus on living. Build 2–3 of these models, and they can cover your core expenses.

But I must repeat: e-commerce can lose you money. You can go into loss. You can also reach serious profits. The determining factor is what you actually know.

The Mindset Shift That Took Me 21 Years

The biggest mistake I see is treating business like a lottery ticket. People ask: “I paid X for this course, when will I make it back?” or worse, “When will you make it back for me?” This mindset belongs nowhere near commerce. Even million-dollar companies do not expect to recover a new store’s opening costs the next day. Realistically, they aim to break even over a year, sometimes recovering half, sometimes all of it. Then everything after is long-term profit.

Think of it like buying a house. You do not purchase a £10 million property and ask if you can earn £10 million tomorrow. You calculate: this generates £1 million monthly, so in 10 months I recover capital, and everything after is mine. That mathematical patience is what separates people who build wealth from people who chase it.

My AI systems gave me something beyond money: they let me ask “What do I actually want from life?” instead of “How do I survive today?” That question — what you want, how to get there, what to improve — is where real progress starts.

FAQ

How much money do I need to start e-commerce with AI automation?

For eBay dropshipping or affiliate marketing, you can start with minimal capital — often under £100 for basic store setup. For Amazon FBA, I consider £5,000 the absolute floor and £5,000–£15,000 a realistic range. The AI automation tools themselves vary; my custom systems required significant time investment (2.5 months, ~10 hours daily) rather than pure cash, but commercial alternatives exist at different price points.

Can AI really handle customer service without sounding robotic?

In my experience, yes — with proper setup. My messaging agent maintains conversation context across days, researches current platform data before responding, and improves with each interaction. The key is feeding it accurate, updated information and training it on your specific business parameters. Voice cloning and video generation are technically possible but require additional API configuration.

Is the crypto trading AI you mentioned safe to use?

I do not present it as safe or recommend it. I tested it with approximately £5,000, made roughly £1,100, and withdrew. I view it as experimental technology, not a business strategy. The risks in automated crypto trading are substantial, and my test was designed to understand mechanics, not to build a revenue stream.

How long does it realistically take to learn e-commerce properly?

Based on my 21 years in the field, I estimate 3 years for solid competency when learning independently — including watching courses, understanding, applying, failing, testing, and building experience. Compressed timelines depend heavily on mentorship quality, hours invested, and starting knowledge. Someone with guidance from an experienced operator can potentially reduce this significantly, but there is no genuine substitute for lived experience.

Final Thoughts

I have uploaded nearly 4,000 videos as a diary of what I learned, sold, failed at, and achieved. None of it comes with a guarantee that it will work identically for you. Something that failed for me might thrive for you, or vice versa. The value is in analyzing what fits your situation without repeating my mistakes blindly.

The tools will keep evolving. In a month, I might laugh at what I built today as primitive. The direction — thought-controlled systems, deeper integration — is already beginning. But the goal stays constant: build systems that let you live while they work. If you are waiting for permission to start, this is it. Move. Learn. Apply. And if you can, surround yourself with people who have already walked the path you want to take.

Everything I share is based on my own experience. Your results will depend on your effort, time, knowledge, and market conditions. There are no guarantees in commerce — only probabilities you can improve through preparation.

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 AI Agents That Run My £16K/Day Stores — Here’s the Real Setup appeared first on Digital Market Mentoring.

Key Takeaways

• Cloud Code now runs 7-10 simultaneous AI agents for product research, customer messaging, cold email, and more
• I made £850 in one day from a single affiliate system running on autopilot; my daily target is £1,000
• Setting up the system took 3-4 hours initially (not 10 minutes as I first claimed), but now runs 24/7 without me
• I manage roughly 50-100 social media accounts and platforms through this automation
• Free LLM models via Ollama eliminate subscription costs — no API fees needed for most users
• The real advantage isn’t “easy money” — it’s building a system that compounds while you sleep

Why I Believe 2026 Changes Everything

I’ve been in this game for 21 years, working minimum 10 hours daily. What I’m seeing now with multi-agent AI systems isn’t just another tool — it’s a fundamental shift. Cloud Code and similar platforms now let you deploy 7, 8, 9, 10 agents working simultaneously: one does market research, another handles customer messages, another sends cold emails, another optimizes your eBay listings.

Here’s what most people miss: by 2027, I believe we won’t even be configuring these systems manually. Physical robots will execute the automations we’re building today. If you don’t have the underlying infrastructure — the agents, the workflows, the API connections — you’ll be technologically stranded. The phone in your hand might even become obsolete.

This isn’t hype. I’ve already automated systems for companies that previously employed 10 customer service staff at 500,000 Turkish Lira monthly. Those roles disappeared after my integration. Whether that’s “good” or “bad” is debatable — what’s undeniable is that it’s happening.

My Exact Setup: From Zero to Running Agents

Let me walk you through what I actually did, step by step.

Step 1: I downloaded Ollama to my computer. This gives you free access to powerful LLM models without paying subscription fees. I still pay for some APIs because my volume is extreme, but for most people building their first system, the free tier is sufficient.

Step 2: I connected these free LLM models to Cloud Code (you can also use Hermes — technically they’re nearly equivalent right now). Cloud Code has paid tiers, but if you run it locally through Ollama, it’s free.

Step 3: I configured 7 agents to work in sequence. The first researches markets. The second creates optimized eBay titles and images — but critically, I ensure real product photos are included, not AI-generated fakes. The third handles affiliate content creation. The fourth distributes across platforms. The fifth engages with incoming messages. The remaining agents handle analytics, feedback loops, and scaling.

For someone with just 4-5 accounts, this entire setup might take 10 minutes. For me, with 50-100 social media properties, it took 3-4 hours of configuration.

The £850 Day: What Actually Happened

Let me be brutally honest about what “passive” means here. I went to the gym. I came back. My affiliate system had generated £850 in sales. The money hit my account — £857 after bank fees, to be precise.

But here’s what that required: I built this specific affiliate system over hours of work. I registered with affiliate companies. I created AI-powered content. I distributed it across approximately 100 platforms. I set up automated messaging so when people engaged, the system responded instantly.

Today, that system runs while I sleep. I’ve set a target of £1,000 daily from this single stream alone. I hit it once already. I believe I can make it consistent.

Is this “easy money”? No. I worked 21 years to understand how to build systems that work. The £850 day wasn’t luck — it was the output of a machine I constructed carefully over time.

Why Most People Fail (And How to Avoid It)

I speak to hundreds of people daily. The single reason 90% fail? They focus only on making money, not solving problems. They’re obsessed with “I need to earn” rather than “I need to provide value.” This mindset is fatal.

Here’s my practical method for finding a viable model: spend 1-3 months shopping normally on Amazon, eBay, Trendyol, or whatever platform you use. Notice what you actually buy. What books do you read? What sports equipment? What beauty products? Buy them. Use them. You now have authentic experience that no competitor can fake.

When I want to sell PDFs, I don’t guess — I speak to my computer, and within 30 seconds to 5 minutes, I generate dozens of PDFs distributed across 100 platforms. But the content works because I know the subject deeply.

The people who succeed in my community aren’t the smartest — they’re the ones who take action. They register for affiliate programs. They install Ollama. They configure their first agent. They post their first piece of content. The ones who just ask questions and never move? They stay exactly where they started.

The Real Business Model: Build Once, Sell Forever

Here’s what I charge companies for automation builds: £5,000 to £15,000 per system. What do they get? A 24/7 operation that replaces multiple employees.

But you don’t need to sell to others. Build for yourself first. The logic is simple: if AI can do 50% of your current job (and I assure you, for most online business tasks, it can), then employers will eventually make the same calculation I do with clients. Why pay you when a system works continuously without sleep, without breaks, constantly self-improving?

My own system now applies to roughly 4,000 companies I’ve connected with. They all produce content, they all distribute, they all engage — automatically. I don’t manually manage each one. The agents handle it.

FAQ

What’s the difference between Cloud Code and Ollama?

Cloud Code is the agent orchestration platform that runs multiple AI workers simultaneously. Ollama is the free software that lets you run large language models locally on your computer without API subscription costs. I use Ollama to power Cloud Code’s agents for free. Hermes is a comparable alternative to Cloud Code with similar technical capabilities.

How much does this actually cost to start?

Zero pounds if you follow my path: Ollama is free, Cloud Code is free when run locally, and affiliate programs are free to join. I pay for some APIs because my business volume requires extreme throughput, but beginners don’t need this. The real investment is time — expect 3-4 hours for proper initial setup if you have multiple accounts, or potentially 10 minutes for a simple 4-5 account configuration.

Can this really replace a full-time income?

I won’t promise that. What I can verify: I made £850 in one day from one affiliate stream, and my systems generate consistent sales. However, I’ve worked 21 years in this industry, and I built these systems over hours of focused work. The income becomes “passive” only after significant upfront construction. Some of my students succeed; some don’t. The differentiator is consistent action, not the tool itself.

Why are you sharing this if it works so well?

I’ve learned that information and money are meant to circulate. I’ve helped enough people that I receive constant messages of gratitude — and paradoxically, this generosity returns to me multiplied. When you help others succeed, you build networks, reputation, and unexpected opportunities. My £10,000 client deals often come from people I initially helped for free. Plus, frankly, I enjoy this. I was livestreaming when I had 2 viewers; I still do it now. The teaching itself is the reward.

Final Thoughts: The Window Is Now

I’m not saying everyone must do e-commerce. I’m not saying everyone must get rich. What I’m saying is this: the people who build automated systems in 2026 will operate on a different plane than those who don’t.

I’ve created what I call a “monster” in the positive sense — a system behind me that works automatically, generates income, and lets me focus on what I actually enjoy. You can build for yourself, or eventually, someone like me will build it for your employer. I’d rather you controlled your own trajectory.

Download Ollama. Download Cloud Code or Hermes. Configure your first 7 agents. Start with affiliate marketing if you have no capital. Create content with AI, distribute it, let the system handle responses. The technology is here. The only variable is whether you’ll use it before the window shifts again.

“I built this system. I went to the gym. I came back to £850. That’s not magic — that’s infrastructure. And infrastructure is something anyone can build.”

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 £850/Day AI Agent System — Here’s the Exact Setup appeared first on Digital Market Mentoring.

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.

I saw a tweet with 3 million views. Nous Research had released an open-source AI tool called Hermes Agent. At first, I didn’t think much of it — every week there’s a new AI promising to revolutionize everything. But this one was different. I checked GitHub: over 23,000 stars in just 2 weeks. By week 7, it hit 100,000 stars. No AI agent in GitHub history had grown this fast. I sat down, tested it, and I’ve been using it for 10 days now. And I need to be straight with you: this doesn’t behave like ChatGPT, Cursor, or Claude. Because Hermes remembers you.

Key Takeaways

• Persistent memory: Hermes remembers who you are, what you do, and your projects — permanently
• 24/7 operation: Runs on a $5 VPS server, works even when your laptop is off
• Multi-platform access: Control via Telegram, Discord, Slack, WhatsApp, and 11 other platforms
• Skill learning: Saves solutions as “skills” and improves with each repetition
• Sub-agents: Parallel AI workers that can be specialized and coordinated
• Setup time: Approximately 10 minutes for basic installation
• Cost: Open-source, MIT licensed, and free to use

Why Hermes Feels Like Hiring an Employee, Not Using a Tool

The first thing that struck me was the memory. With ChatGPT, every conversation starts from zero. With Hermes, I tell it once who I am, what I do, and what I’m working on. The next day, I don’t need to repeat anything. It just knows.

Here’s a concrete example from my testing. I told Hermes: “Every morning at 9 AM, send me the top-selling products on eBay UK via Telegram, formatted as a report.” That’s it. One instruction. Now every morning, the report arrives. While I sleep, a system running on a $5 server works like a machine. This isn’t a chatbot — it’s a persistent employee that learns your business.

The second critical difference: Hermes doesn’t live on your laptop. It lives on a server. I configured mine on a cheap VPS. I can close my laptop, go out, and still send commands from my phone. “Post this week’s content to YouTube, push my eBay sales.” Two weeks later, normal response. The agent keeps working.

How the Skill System Creates Compound Returns

This is where Hermes diverges fundamentally from every other AI I’ve tested. When Hermes solves a problem, it saves that solution as a “skill.” The second time it encounters the same task, it doesn’t start from scratch — it continues from where it left off. By the third, fourth, tenth time, it’s faster and better.

I need to be honest: this sounds simple when I say it, but when you actually experience the difference, it changes your perspective on what AI can do in your business. You’re no longer renting a tool by the conversation. You’re building an asset that appreciates.

In my 10-day test, I watched Hermes develop approximately 149 skills in my dashboard. Tasks that initially took several minutes of back-and-forth now execute automatically. The agent maintains a Kanban-style memory system that tracks what it’s learned and what it’s working on.

Setting Up Hermes: What Actually Works

The basic setup takes about 10 minutes. You copy a command, paste it in your terminal, and the rest runs automatically. You select your AI model — Claude, GPT, Gemini, whatever you prefer — connect your Telegram bot, and it works. Everything is open-source, MIT licensed, and free.

However, I need to flag something important from my testing. Hermes has an extensive skills marketplace where you can download additional capabilities — social media automation, browser control, research tools, API integrations. The temptation is to install everything. Don’t. Each skill consumes tokens and storage. I made this mistake early: loading too many skills clogged my system. Be selective. Install only what you’ll actually use.

For API access, I tested the Opera Router option. In just 5 minutes of use, it burned through $10 worth of tokens. My recommendation: use the free tier models, or filter for “free” options in the model selection. This keeps costs manageable while you’re learning the system.

Sub-Agents: The Feature That Changes Everything

The most powerful aspect of Hermes, in my view, is the sub-agent system. You can create parallel AI workers under your main agent, each with different specializations. But here’s what most people get wrong: you need to run these in the correct mode — discovery, activation, and execution.

First, the research agent finds and develops its own capabilities using its existing skills. This self-improvement loop is genuinely different from anything I’ve seen in other platforms. Then you activate the specific skills needed, and finally execute. I’ve seen hundreds of people online burning tokens because they skip these steps and don’t understand what their agents are actually doing.

In my own setup, I configured separate sub-agents for: morning eBay research, weekly analytics analysis, YouTube content research, and other business tasks. Each runs on its own schedule, reports back via Telegram, and improves its performance over time.

Security and Practical Considerations

When setting up Telegram integration, create your bot token through BotFather and never share that API token externally. This maximizes your security. I also recommend running Hermes on a virtual server rather than your local machine if you’re loading heavy browser-automation skills — this prevents performance degradation on your main computer.

For those who prefer not to use the terminal, Hermes Desktop now exists. You download it as a standard Mac application and run everything through a graphical interface. The functionality is identical — it’s simply a different access method.

What I Honestly Think After 10 Days

Hermes is not perfect. The setup requires more technical comfort than ChatGPT. You need to understand tokens, API keys, and basic server concepts. The documentation, while extensive, assumes some technical background. And if you’re careless with skill installation or model selection, costs can escalate quickly.

But the fundamental architecture is correct. Persistent memory, skill accumulation, server-based operation, and multi-platform access — these aren’t features, they’re a different category of tool. Where ChatGPT is a brilliant conversationalist you meet anew each time, Hermes is a colleague who learns your business and works continuously.

I’ve been following AI developments closely, and I’m among the first in Turkey to test and document this tool. The industry hasn’t fully recognized what’s happening here yet. The growth metrics — 100,000 GitHub stars in 7 weeks — suggest the developer community sees it clearly.

FAQ

How much does Hermes cost to run?

The software itself is free and open-source under MIT license. You’ll need a server (approximately $5/month for a basic VPS) and API access to an AI model. Free tier models are available; paid models vary by usage. In my testing, careless model selection cost $10 in 5 minutes, while careful configuration runs for minimal cost.

Do I need coding skills to set up Hermes?

Basic setup takes about 10 minutes and requires copying commands into a terminal. For non-technical users, the community provides step-by-step guides, and you can paste instructions into AI assistants like Claude to generate setup commands. A desktop application is also now available for Mac that eliminates terminal use.

How is Hermes different from ChatGPT or Claude?

Unlike conversational AIs, Hermes features persistent memory (remembers your identity and projects permanently), runs continuously on servers (works when your devices are off), learns and saves skills that improve with repetition, and operates across 15+ platforms including Telegram, Discord, Slack, and WhatsApp.

What are the main risks or downsides?

Token costs can escalate if you use premium models or install excessive skills. The system requires more technical setup than consumer AI tools. Skill bloat — installing too many capabilities — can slow performance. And as with any server-based system, API token security is your responsibility.

Conclusion

After 10 days of testing Hermes, I’m convinced this represents a genuine evolution in how solo entrepreneurs and small teams can leverage AI. Not as a smarter chatbot, but as a persistent, learning system that operates continuously across the platforms you already use.

The setup investment — roughly 10 minutes plus learning time — pays dividends in automated workflows that improve themselves. My morning eBay reports, weekly analytics, and content research now run without my involvement, getting slightly better each cycle.

If you’re currently using AI by opening and closing the same chat window daily, you’re hammering that same nail. Hermes offers something different: building a machine that remembers, learns, and works while you focus elsewhere. It’s not guaranteed income or effortless automation. It requires thought, configuration, and ongoing management. But in my direct experience, it’s the most capable open-source AI agent available today, and its growth trajectory suggests I’m not alone in that assessment.

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 Tested Hermes AI Agent for 10 Days — Here’s What ChatGPT Can’t Do appeared first on Digital Market Mentoring.

Here’s what struck me most: I’ve spent three years building systems that earned me 1.5 million points (roughly £1.5 million in revenue). This person burned nearly that much in 30 days. That gap between efficient and catastrophic AI agent deployment is exactly what I want to close in this article.

Key Takeaways

• Token repetition is the silent killer: AI agents re-reading the same files caused the $1.3M disaster
• Code Graph reduces token costs by ~95%: Map your codebase once, query the map instead of re-reading files
• Match the model to the task: Claude Code for complex work ($20-100/mo), Codex for speed ($10-50/mo), Hermes for local/free
• My three-layer system costs $30-50/month: Single tasks → Code Graph memory → full automation with Telegram control
• Three non-negotiable security rules: spending limits, mandatory Code Graph, and model-task matching

Why AI Agents Burn Money: 3 Root Causes

When I dissected that $1.3 million case, I found three predictable failure patterns. I’ve seen solo developers and small teams repeat them constantly.

1. Repetitive File Reading

Every time an agent receives a task, it insists on reading the necessary code fresh. Five minutes later, another task hits the same file. Then another. The same 50 files, hundreds of thousands of tokens each cycle. No memory, no caching, no learning.

2. Maximum Context Window Abuse

Claude Sonnet 4.5 offers a 200,000-token context window. The agents in that case maxed it out every single request. At roughly $1 per 200K tokens, one agent doing this repeatedly becomes expensive fast. Now multiply by 100 agents running in parallel.

3. Parallel Agent Overload

The developer ran 100 agents simultaneously. Each loaded its own full context. Simple math: 100 agents × 200,000 tokens × constant requests = 1.3 million dollars in one month. The system was architecturally sound but economically naive.

“This system didn’t fail—it was too successful at spending money.”

Code Graph: The 95% Cost Reduction Nobody Talks About

Code Graph sits at #1 on GitHub trends for good reason. It solves the repetition problem at its root.

Here’s how it works: instead of agents reading your entire codebase for every task, Code Graph scans your files once and generates a summary map. When an agent needs to know “what does X function do?” it queries the map—not the original 50 files.

The numbers are stark. Without Code Graph: 50 files read, 100,000+ tokens burned, repeated endlessly. With Code Graph: initial mapping pass, then ~5,000 tokens per subsequent query. That’s where my claimed 95% savings come from.

Code Graph is open-source and integrates with Claude Code, Codex, and Hermes. I run it locally for sensitive projects. For my public-facing automation work, I use it as the memory layer that prevents my agents from “forgetting” what they just read.

Choosing Your Agent: Claude Code vs. Codex vs. Hermes

I use three main agent brands, each for specific situations. GitHub’s February announcement that Claude and Codex now integrate directly into repositories made this even more practical.

Claude Code: Quality at a Price

Claude Code is Anthropic’s agent with strong reasoning capabilities and low error rates. I deploy it for complex coding tasks and customer-critical work where mistakes cost more than tokens. Monthly cost: $20-100. Think of it as the senior developer who won’t stop until the goal is complete.

Codex: My Workhorse for Speed

I use Codex most heavily. It’s fast, cheap, and operates smoothly for repetitive tasks and automations. Monthly cost: $10-50. I’ve integrated it with my phone so I can issue commands without sitting at my desk. It connects across my 4-5 computers and executes with approval workflows.

For social media posting workflows specifically, Codex has been incredibly stable and fast. I describe what I need, and it handles notes, scheduling, and execution in seconds.

Hermes Agent: Local and Free

Hermes runs open-source on your own machine. If you’re handling sensitive data, want zero internet exposure, or need zero marginal cost, this is your option. I use Hermes for eBay automation and any workflow involving API tokens I don’t want externalized.

Combined monthly total across all three: $50-200. Annually, that’s roughly £1,000 in service fees versus the $1.3 million monthly burn of the headline case.

Gemini Spark and the 2026 Personal Agent Race

Google’s Gemini Spark represents something bigger than one product. Every major company—Google, OpenAI, Anthropic—has entered the personal AI agent race. I believe this will be 2026’s defining technology wave.

Spark specifically reads your emails (naturally, Google owns Gmail), monitors your calendar, auto-responds, and suggests meeting plans. It follows the same philosophy as OpenHuman, which I also track closely.

OpenHuman operates as a “personal AI superintelligence” that learns from your GitHub, Stripe, calendar, and email patterns without waiting for prompts. It proactively develops itself. I watched it select my daily priorities from my inbox, request my self-hosting preference, then begin autonomous setup.

The race isn’t just about features anymore. It’s about which system can deliver automation faster and more reliably to regular users. Speed of deployment will determine winners.

My 5-Step Security System: $30-50/Month

This is the practical framework I promised. I’ve refined it through three years of building systems that now generate over £20,000 monthly, with single days hitting £16,000 in sales.

Layer 1: Single Tasks (Week 1)

Give agents one task, one prompt, one output. “Write this specific customer response.” Daily cost: approximately $0.30. No context carryover, no memory, just execution.

Layer 2: Code Graph Memory (Week 2)

Add Code Graph so agents remember previous tasks. They stop re-reading the same files. Daily cost rises slightly to ~$0.50, but capability multiplies.

Layer 3: Full Automation (Week 3)

Connect n8n to a Telegram bot. Agents run autonomously. You control everything from your phone. Daily cost: ~$1.00. Monthly: $30. Annual: $365.

The 3 Mandatory Security Rules

1. Set hard spending limits. Configure your API dashboard with a $50-100 ceiling. When hit, everything stops until you manually review and reset. Non-negotiable.

2. Code Graph is mandatory. I mean this literally: do not deploy AI agents without Code Graph. The 95% token savings isn’t optimization—it’s survival.

3. Match model to task complexity. Simple tasks with Claude Sonnet wastes money. Use Haiku—10× cheaper. Complex tasks with Haiku wastes time and produces errors. Task-appropriate model selection is a discipline, not a preference.

I use OpenRouter’s leaderboard to validate my choices. Hermes currently leads both “top agent coding” and “top proactive” categories, with Claude Code at #4. But “most popular” doesn’t mean “right for you.” DeepSeek V4 Flash might be cheapest for your specific input/output ratio. Analyze the per-million-token pricing yourself.

“Sometimes everyone’s right answer is your wrong answer. Your use case determines your truth.”

FAQ

What exactly caused the $1.3 million AI agent bill?

Three factors combined: agents repeatedly re-reading identical files, maxing out 200,000-token context windows on every request, and running 100 agents in parallel with no shared memory. The system functioned correctly but had no cost controls.

What is Code Graph and why does it save 95% on tokens?

Code Graph is an open-source tool that creates a searchable map of your codebase or documents. Instead of agents reading full files for every task, they query the pre-built map. First scan: full read. Subsequent queries: ~5,000 tokens instead of 100,000+.

How much does your AI agent system actually cost per month?

My three-layer system costs approximately $30-50 monthly: $0.30/day for single tasks, $0.50/day with Code Graph memory, $1/day for full automation via Telegram. My total across Claude Code, Codex, and Hermes ranges $50-200/month depending on workload.

Which AI agent should I choose for my specific needs?

Use Claude Code for complex, error-sensitive work ($20-100/month). Use Codex for fast, repetitive automation ($10-50/month). Use Hermes for local processing of sensitive data (near-zero cost). Match the model to task complexity—never default to the most powerful option.

Conclusion

The gap between $1.3 million monthly burn and my $30-50 system isn’t about having less ambition. It’s about architectural discipline. Code Graph eliminates repetition. Model matching prevents overpayment. Spending limits prevent surprises.

I’ve built systems generating £20,000+ monthly and £1.5 million total over three years. The same tools that could bankrupt a careless operator can empower a methodical one. The technology has arrived. Your economic survival depends on how you deploy it.

My next deep-dive will cover Code Graph installation step-by-step. If you’re building AI agent systems—or trying to avoid becoming the next headline—start with these three security rules before you add a single agent.

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 How I Run 100 AI Agents for $30/Month (Not $1.3M) appeared first on Digital Market Mentoring.

Key Takeaways

• $10/month starting price replaces tools that cost me £250–300 combined
• DeepSeek Computer Use Agent controls your actual desktop, browser, and applications — not just chat
• 31 AI models included: GPT-4, Claude, Gemini, Grok, Llama, and others in one interface
• Built-in meeting assistant records, transcribes, and lets you query conversations in real-time
• Website and plugin builder works from natural language prompts without coding

What “Computer Use” Actually Means in Practice

Most AI tools I test are glorified chatbots. DeepSeek Computer Use Agent, which Abacus AI integrated recently, is different. It can literally see and control your computer screen. I watched it open my browser, navigate websites, fill out forms, scrape data, and write code — all while I had my hands off the keyboard.

Here’s a concrete example from my testing. I asked it to research a topic online, and within seconds it had opened its own browser instance (DeepThing Agent), found relevant information, and started analyzing applications. Not suggesting what I should do — actually doing it. This isn’t a soft web robot running in a sandbox. It’s an AI that works.

For someone running an online business, this changes the math completely. Tasks that used to eat 3 hours of my day — like sourcing product information, filling out repetitive forms, or monitoring competitor sites — now run in the background while I focus on strategy.

Replacing My £300 AI Stack With One Subscription

Let me be transparent about my actual costs before Abacus AI. I was paying for:

• GPT-4 and other OpenAI models
• Claude (Anthropic)
• Google Gemini
• Grok access
• Lovable and Bolt for frontend building
• Hostinger for hosting experiments
• Replit AI for quick deployments

That totaled roughly £250–300 monthly, and I was still context-switching between platforms constantly. Abacus AI gives me 31 AI models in one interface — including all the ones I was paying for separately. I can switch between DeepSeek, GPT-4, Claude, Gemini, Grok, Llama, and others without managing multiple subscriptions.

The cost difference is almost comical. $10 per user per month for the starter plan that includes the desktop agent, browser automation, and model access. I was paying 25x more for a fragmented experience.

The Meeting Feature That Caught Me Off Guard

I didn’t expect to care about this. I’ve tried dozens of transcription tools. But Abacus AI’s meeting assistant is built differently — it records not just audio, but the actual system context of what’s happening on your screen.

Here’s how I tested it. I opened one of my own YouTube videos (about 8 minutes 48 seconds long), clicked the “Listen” feature, and let it run in the background. The system recorded everything being discussed, timestamped it, and built a queryable knowledge base from the content.

Then I tested the real value: I asked it a specific question about something mentioned in the video — “how to start with e-dropshipping” — and it gave me the exact answer with context, within seconds. It didn’t just search a transcript; it understood the content well enough to answer naturally.

For online meetings, this means you can have your camera off, be partially distracted, and still participate intelligently. The system records everything, and if someone asks you a question, you can query the conversation in real-time and get a coherent response. I’ve used this in team calls and client meetings where I needed to multitask. It’s not perfect — you still need to review the substance — but it removes the anxiety of missing critical details.

For students in online classes or anyone in back-to-back Zoom calls, this is genuinely useful. The recordings and conversation transcripts are saved for later review too.

Building Websites and Plugins Without Code

I’m not a developer. I know enough to be dangerous, but I hire out real engineering work. So when I tested the website builder, I was looking for something that could produce a functional starting point — not a finished product.

I used their prompt system to request an e-commerce site. The agent asked me specific questions first: brand name, domain preference, target countries, color scheme, payment methods (Stripe, PayPal, etc.), shipping approach, and language setup. I appreciated this — it wasn’t generating generic templates, it was trying to build something configured for my actual needs.

My recommendation based on testing: set it up in English with Turkish language support included. This gives you the broadest reach for international selling. The system handles tracking numbers, regional targeting, and basic store infrastructure.

For plugins, it’s similarly straightforward. You describe what you need in natural language, and the agent generates something you can deploy. I tested this with a simple Chrome extension concept, and the initial code structure was usable within minutes. You can sell these on marketplaces or use them for your own operations.

Important caveat: the site builds a foundation. You still need to add your actual product details, verify payment integrations, and handle compliance. But for getting from zero to something functional in seconds rather than weeks? That’s real time saved.

Automating the Boring Stuff That Actually Costs Money

The biggest hidden cost in any online business is administrative drag. Answering repetitive emails, scheduling, data entry — these don’t generate revenue directly, but they consume the time you could spend on things that do.

I connected Abacus AI to my email workflow as a test. It can draft responses, optimize tone for professionalism, and handle routine inquiries. For team operations, it integrates with meeting schedules and task management. The “Deep Research” section generates professional reports and presentations.

Here’s the principle I operate by: time saved is money earned. If you’re spending 3 hours daily on Twitter content, and an agent reduces that to 15 minutes of review, you’ve bought back 2.75 hours. That’s time for product development, customer acquisition, or actually living your life. This isn’t hypothetical — I’ve tracked my own weeks before and after implementing these automations.

What I Actually Downloaded and How It Works

Getting started requires two things: the Abacus AI web interface and the Deep Agent Desktop application. The desktop component is what enables the computer control features — browser automation, application interaction, and system-level tasks.

Installation took under two minutes. The system auto-detected my hardware and configured voice command support accordingly. You can run everything browser-based if you prefer, but the desktop agent unlocks the full capability.

From the chat interface, you select “Deep Agent” mode, then describe what you want done. The agent plans the steps, executes them on your system (with your permission), and reports back. I watched it build a project structure, research competitors, and draft code — all from single-sentence prompts.

FAQ

Is Abacus AI really only $10 per month?

Yes, the starter plan with DeepSeek AI Agent and desktop control starts at $10 per user monthly. Higher tiers exist for team features and increased usage, but the core functionality I tested is available at this price.

Do I need coding skills to build websites with this?

No. The agent asks you structured questions about your business — products, branding, payments, shipping — and generates functional sites from natural language prompts. Basic technical literacy helps for customization, but no programming is required for the initial build.

Can it really replace ChatGPT, Claude, and other AI tools?

For my workflow, largely yes. Abacus AI includes 31 models spanning GPT-4, Claude, Gemini, Grok, Llama, and others. If you need specific features exclusive to one platform, you might keep it, but I canceled multiple subscriptions without losing capability.

Is the meeting assistant recording legal and ethical to use?

Legality varies by jurisdiction — always inform participants you’re recording. Ethically, I use it for my own meetings where I’m a participant, and I disclose when asked. The feature is designed for personal productivity, not surveillance.

Final Thoughts

After three decades of testing AI tools that overpromise, I’m cautious with recommendations. Abacus AI’s DeepSeek Agent isn’t magic — it won’t run your business for you, and it makes mistakes that need human correction. But the combination of genuine computer control, unified model access, and aggressive pricing at $10/month makes it the most cost-effective automation platform I’ve found in 2025.

I replaced approximately £250–300 in monthly subscriptions. More importantly, I reclaimed hours weekly that were going to context-switching between tools and handling repetitive tasks. For online business owners, e-commerce sellers, and anyone building toward economic independence through automation, this deserves serious testing.

If you try it, I’d genuinely like to hear your results — especially if you’re using it for e-commerce or content workflows. Drop your experience in the comments.

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 Tested Abacus AI’s DeepSeek Agent — $10 vs £300 AI Stack appeared first on Digital Market Mentoring.

Key Takeaways

• DeepSeek V4 Pro is now permanently priced at $0.435/million input tokens and $0.87/million output tokens — 75% below its April 2026 launch price
• Compared to GPT-5.5 at $30/million output tokens, that’s roughly a 34x price difference; compared to my Claude usage, nearly 86x cheaper
• On LLM leaderboards, DeepSeek V4 Pro Max scores nearly identically to Claude Mythos for general intelligence, coding, and agent creation
• Three critical weaknesses: no native image/video/audio processing, Chinese servers raise data sensitivity concerns, and long multi-step agent loops still favor Claude Opus 4.7
• Three types of people should switch immediately: n8n/API builders, high-volume content automation users, and AI agencies reselling to enterprise clients

The Price Cut That Wasn’t Supposed to Last

DeepSeek V4 Pro launched on April 24, 2026, at $1.74/million input tokens and $3.48/million output tokens. Days later, they announced a 75% discount — but with a catch. The promotional pricing would expire May 31, 2026. Everyone marked their calendars, expecting prices to snap back.

Then on May 22, DeepSeek made a new announcement: the price is permanent. Forever. No going back. I pulled up the pricing page myself to verify. Input tokens at $0.435 per million. Output tokens at $0.87 per million. That’s essentially the launch price divided by four, locked in indefinitely.

Here’s the math that made me pay attention. GPT-5.5 charges $30 per million output tokens. DeepSeek charges $0.87. That’s not a minor efficiency gain — that’s a fundamentally different cost structure. When you’re running 300 agents simultaneously, as I do in my backend systems, those multipliers compound into real money.

Benchmark Reality Check: Does Cheap Mean Bad?

I went straight to the LLM leaderboards to see what the numbers actually say. Here’s what I found.

For general intelligence, Claude Mythos currently ranks at the top. For coding, Mythos again. For agent creation, same result. But Mythos isn’t publicly available — it’s locked behind enterprise deals with major corporations. When I looked at what’s actually accessible to builders like us, DeepSeek V4 Pro Max shows performance that’s “nearly identical, up and down” to Mythos on the same benchmarks.

The leaderboard breakdown by use case is worth studying. If you need image generation, ChatGPT Image 2 leads. Video creation favors Veo. Text-to-speech and speech-to-text each have their own specialized winners. But for core LLM tasks — the text reasoning, coding, and agent orchestration that powers most business automation — DeepSeek V4 Pro sits at or near the top of available models.

I’ve been running DeepSeek inside my Hermes agent orchestration system for text processing while reserving Claude for visual tasks. The cost savings let me run 59 agents in my cloud setup instead of the 4 large controller agents I was limited to before.

My Real-World Test: Product Research Automation

I wanted to test this with something concrete, not theoretical benchmarks. I went to ChatDeepSeek.com, created a free account, selected V4 Pro with Thinking Mode enabled, and gave it a prompt I actually use in my e-commerce business:

“You are my expert research analyst. Find 5 under-served but profitable product categories trending in the UK. For each, give me average selling price, competition level, and potential profit margin.”

The context: the UK is currently in peak summer season, and I’ve been successfully dropshipping Turkish products — items like evil eye charms (nazar boncuğu) that sell for £50-100 in UK markets but cost me £50 to source from Turkey. I list them at £500, fulfill at £50, and keep roughly £100 profit per sale.

DeepSeek’s response came in seconds. It identified handwoven rugs, silver jewelry (şahmeran designs), Turkish coffee sets, and several other categories with specific margin calculations. The same prompt on Claude would have cost me 34-86 times more to run. For my eBay listing automation — where I instantly push found products to my store — this cost difference transforms what’s economically viable to automate.

How I Integrated DeepSeek Into My 24/7 Agent System

The real power isn’t the chat interface — it’s the API. Here’s exactly how I set it up.

Step 1: I went to platform.deepseek.com, registered with my email, and navigated to the API key section. New accounts currently receive 5 million free tokens — more than enough for extensive testing. I created a key labeled “Hermes-agent” for my orchestration system.

Step 2: In n8n, I selected the OpenAI node. DeepSeek is OpenAI-compatible, so you only need to change the base URL to DeepSeek’s API endpoint and paste your key. The templates are already available — I use them for social media automation, video creation workflows, and multi-platform publishing.

My current stack runs like this: DeepSeek handles text reasoning inside Hermes, which orchestrates my agent swarm. Claude still handles visual processing when needed. Antigravity manages frontend UX. The system auto-analyzes performance, generates improvement reports, stores learnings in memory, and iterates — literally running 100x better the next day.

I’ve also built affiliate automation on top of this. My AI agent creates content daily, embeds my affiliate links automatically, and publishes across 20+ social platforms. In 90 days it generated nearly zero income. But over the past year, that same automation has produced approximately $4,000, with lifetime earnings near $73,000 — entirely passive once configured.

Important caveat: This isn’t guaranteed for everyone. Building AI automation is now possible for maybe 50% of people with technical aptitude. But building automation that actually makes money — that requires understanding who to sell to, what service to offer, and how to reach buyers. The AI can’t do that strategic thinking for you.

Three Critical Weaknesses I Found

I’m not going to pretend this is a universal Claude killer. I identified three specific limitations in my testing.

First: No native multimodal processing. DeepSeek V4 Pro handles text only. No image generation, no video analysis, no audio processing. For my visual workflows, I still route to GPT-5.5 or Claude. My agent flow is explicitly designed around this — text to DeepSeek, visuals to specialized models.

Second: Data location concerns. DeepSeek’s servers are in China. For sensitive customer data, travel records, or confidential business information, I don’t route through their API directly. Self-hosted alternatives, Western API proxies, or solutions like Bluehost provide more secure paths when data sovereignty matters.

Third: Long-horizon agent loops. For terminal agents, extended multi-step automations, and complex cyclic workflows, Claude Opus 4.7 still maintains an edge. DeepSeek wins on short and medium tasks. My rule: mixed deployment based on task complexity.

Who Should Switch Immediately

Based on my testing and cost modeling, three specific profiles should prioritize this transition.

API builders and n8n users: If you’re writing custom scripts and can’t afford to rewrite code constantly, DeepSeek V4 Pro offers the best price-to-performance ratio available. The OpenAI compatibility means zero code changes beyond base URL and key.

High-volume content automators: I was spending roughly $20/month just on text generation for my content pipeline. DeepSeek cuts this dramatically while maintaining output quality for my use case.

AI agencies selling to enterprise: This is where the 86x multiplier becomes transformative. I sell automation systems to large companies who previously paid tens of thousands monthly for equivalent capability. My clients now build at roughly 86% lower API cost — a compelling sales argument that essentially markets itself.

FAQ

Is DeepSeek V4 Pro really 86x cheaper than Claude?

Based on my actual usage: GPT-5.5 output tokens cost $30/million, DeepSeek V4 Pro costs $0.87/million — roughly 34x cheaper. My personal Claude cloud API spending averages $200/month versus what would be approximately $2-3 on DeepSeek for comparable text volume, hence the ~86x figure I calculated for my specific workflow.

Can DeepSeek V4 Pro replace Claude for coding tasks?

For most coding tasks, yes. LLM leaderboards show DeepSeek V4 Pro Max performing nearly identically to Claude Mythos on coding benchmarks. However, for extremely long debugging sessions or complex multi-file refactoring spanning hundreds of steps, Claude Opus 4.7 still holds an advantage.

Is it safe to send business data to DeepSeek’s API?

For non-sensitive text processing, I’ve used it without issues. For customer PII, financial records, or regulated data, I recommend self-hosting, using Western API proxies, or keeping that data within EU/US jurisdiction infrastructure. DeepSeek’s servers are in China — factor that into your compliance assessment.

How do I actually connect DeepSeek to n8n automation?

Use the OpenAI node in n8n, change the base URL to DeepSeek’s API endpoint (https://api.deepseek.com), and paste your API key. The platform provides ready templates for social media automation, video workflows, and multi-platform publishing. New accounts receive 5 million free tokens for testing.

Conclusion

DeepSeek’s permanent price cut isn’t just another AI pricing story — it’s a structural shift in what automated business systems cost to operate. I’ve verified the benchmarks, integrated it into my live 24/7 agent infrastructure, and measured the real output quality against models costing 34-86x more.

The model isn’t perfect. It won’t replace your multimodal workflows, and you’ll need to think carefully about data routing. But for the core text reasoning that powers most business automation — product research, content generation, customer service analysis, agent orchestration — DeepSeek V4 Pro delivers at a price point that redefines what’s economically viable to automate.

I’m currently rebuilding my n8n DeepSeek V4 Pro integrations from scratch in my community this week — API setup, base URL configuration, cost optimization templates. The videos are live if you want to follow the exact technical steps.

One honest final note: the technology is now accessible to roughly half the people interested in AI automation. But the technology alone doesn’t generate income. The gap between “can build” and “can sell” is where most people stall — and that’s the part no AI model can solve 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 DeepSeek V4 Pro Is 86x Cheaper Than Claude — I Tested It appeared first on Digital Market Mentoring.

In this article, I’ll show you exactly what those 100 agents produced in 10 minutes — and why the result made me rethink everything I knew about running an online business.

Key Takeaways

• One command launches 100+ parallel AI agents that split a goal into subtasks and work simultaneously
• My four core agents — Cloud (decision brain), Antigravity (content/visuals), Xcodex (development), and Hermes (learning coordinator) — handle research, coding, content, and self-improvement
• A single AI-cloned video reached nearly 1 million views and generated over $73,000 in affiliate revenue within roughly a year — without paid ads
• API costs start from $5 on Open Router, with free tiers available on several tools
• This isn’t magic — it’s system design: the agents learn from mistakes and improve over time

Why I Built This: The Burnout That Broke Me

Let me be honest. Doing this alone is impossible. You need to create content, write code, do research, send emails, track sales, and respond to customers. There are 24 hours in a day, and you’re one person.

I lived this for years. I’d sit at my computer at 6 a.m., look up, and it would be midnight. Why? Because I was doing everything myself.

Then something changed. AI stopped being just a tool you ask questions to. It became something that could actually do the work for you — and not just one instance, but hundreds running at the same time.

What Makes AI Agents Different From Chatbots

Here’s the critical distinction. With a normal AI, you ask a question, get an answer, and that’s it. An AI agent system works completely differently.

You give it a goal. It takes that goal, breaks it into hundreds of pieces, and creates a separate agent for each piece. One does this, another does that, all at the same time.

I call this my “DJ crew.” They don’t ask for salaries, don’t take breaks, don’t get sick. They work at 3 a.m. if I need them to.

When I type a goal — something like “build this system for me” and hit enter — within seconds, dozens of agents activate in the background. Each one runs to a different task. One creates a plan, one writes code, one tests it, one fixes errors. I’m not moving a finger. I’m just watching.

Work that used to eat hours of my day now happens in parallel right in front of me. And no, this isn’t magic. It’s a properly built system.

My Four-Core Agent Stack: How the System Actually Works

The agents don’t just work in isolation. They’re connected across different tools and software, talking to each other. Whichever one is best for a specific domain takes over that task.

Cloud: The Decision Brain

Cloud is the brain behind my operations. The other agents consult it to make the right decisions. With Cloud’s approval, they proceed to execution. I run this on the Opus 4.8 model in the DIN framework — currently the most capable model I can access. I set it to “Effort Max” for the best results, though I should warn you: at the “Deep Note” level, it burns through serious token counts.

Antigravity: Content and Visual Command Center

Antigravity handles all the backend coordination for my content creation — the code, research, and key outputs that need to be visualized and managed so all agents can collaborate effectively. You can use it free up to a certain limit; just download it to your computer. In my setup, I run the DIN model with Gemini 3.5 High Flash, which has given me the best results for content generation. It uses Nano Banana technology for visual production, and the output quality is excellent.

Xcodex: Development and Design Powerhouse

Xcodex handles the development side, particularly UGC and frontend design. It produces professional website designs at a level I couldn’t have imagined before. This is where the visual execution happens.

Hermes: The Learning Coordinator

Hermes is what ties everything together. It connects all the other agents, ensures they learn from their mistakes continuously, and drives their self-improvement. These four agents together monitor everything happening in the world — news, trends, any data point you can think of — checking it in seconds, then executing, developing, researching, learning from errors, and pushing everything to the next level.

The $73,000 Proof: What 100 Agents Actually Built

“Okay, but what do you actually do with this?” That’s the fair question.

I build these systems for large companies, automating work that used to cost them tens of thousands of pounds per month in salaries. After paying me serious money for the setup, these systems work 24/7 for them — never missing a client, constantly improving operations.

But let me show you something more concrete. I told my AI: “Create a video for me.”

The AI cloned me and produced a video on my behalf. It edited it, added text, wrote the description — everything. That video landed on a platform where tens of thousands of people commented. It reached nearly 1 million views on the live channel.

“But Akın, I can record a 5-minute video myself,” you might say. True. But here’s what makes this different: this system learns from its mistakes by analyzing human brain responses. It studies how our brains react to specific colors, patterns, and speech structures — the same way social media platforms keep you watching. My AI analyzes predicted human interaction patterns from the script before ever publishing, then releases the content.

The result? Thousands, tens of thousands of people reached. And here’s the revenue side: I wasn’t even running ads on this. Looking at the roughly one-year period since publication, this single video generated over $73,000. The AI handles responses to commenters too — identifying interested users and sending them links automatically.

Another agent handles distribution across hundreds of social platforms, scheduling at optimal times, researching hashtags, analyzing performance, and learning from errors to improve reach.

How to Build This: The Setup Process

Let me walk you through how to actually build this.

Step 1: Get the foundation. I provide a complete PDF with all materials, links, and my scoring system for the best skills and tools — Open Cloud, Cursor, Codex, Gemini, GitHub, and more. This isn’t just about Hermes; it’s a complete toolkit.

Step 2: Install Antigravity. Download it to your computer. It’s free to use and handles operations fast. You literally just tell it: “Hello, install this for me from this link.” It creates files, asks for permissions (I recommend approving all), and builds your agent in about 3-5 minutes.

Step 3: Fuel your agents with the right models. Think of it this way: you have a car, but do you have gas? The models are your fuel. I use Open Router, where you can get API access for very low costs — sometimes nearly free depending on the model. New users need to load about $5 to access paid models.

Create your API key, set time or spending limits if you want, and you’re connected. My agents use multiple LLMs depending on the task — DeepSeek V4 for certain operations, Gemini Flash for chat, others for specific functions. The key insight: no single model is best at everything. The agents dynamically select whichever model delivers the best results for each specific task.

What This Actually Costs to Run

Let’s be realistic about expenses. You can run Ollama locally for free if you have a powerful computer (about 10GB space needed), but in my experience, it doesn’t run smart enough for serious work. The browser-based approach through Open Router starts at $5.

Model rankings shift constantly. Opus went from 4.7 to 4.8. GPT-5.5 performs well in agent systems. DeepSeek V4 delivers incredible value at very low cost. I’ve checked nearly 50 different LLM ranking dashboards, and they often show very different results — so take any single leaderboard with caution.

What matters is your actual output quality per dollar spent. DeepSeek and Gemini Flash currently lead on cost-efficiency for most agent tasks, while Opus 4.8 ranks #1 on trend metrics despite being 15th on some static rankings. The calculation includes both success rate and cost.

From Theory to Revenue: My Real Numbers

I don’t just talk about this — I live it. My agent system currently works with over 4,000 companies in the background, generating revenue daily. One of my current targets is £100,000 monthly; this month, the system produced approximately $47,200.

I’ve built roughly 15 computer systems in the background that all communicate with each other, cross-referencing and self-correcting. They operate like the world’s best AI experts — one agent might analyze like Elon Musk on marketing, another like Steve Jobs on product, another like Warren Buffett on finance, tracking every token spent and every dollar of revenue. Underneath, 200+ sub-agents each handle specific micro-tasks.

I’ve also applied this to e-commerce: one agent finds products on eBay, researches them, and automatically loads them into my store. I just wait for the sales notifications — hundreds of orders coming in regularly.

FAQ

Do I need coding skills to set up AI agents?

No. I don’t have advanced technical knowledge myself, yet I built my entire agent system using AI. Tools like Antigravity handle the installation automatically — you just paste a link and approve permissions. The PDF guide I provide includes step-by-step instructions with all necessary links.

How much does it cost to start running AI agents?

The minimum viable start is about $5 on Open Router to access paid models. Several tools offer free tiers: Antigravity is free to a certain limit, and Ollama can run locally at no cost if you have sufficient computer power (approximately 10GB storage). My complete setup uses multiple models, but you can begin with a single API key and scale gradually.

Can AI agents really generate income automatically?

My system has generated over $73,000 from a single AI-produced video and approximately $47,200 in recent monthly revenue across operations. However, this required building proper systems, testing, and continuous refinement. The agents learn from errors and improve over time — but they need correct initial setup and ongoing oversight. I never promise guaranteed income; results depend on implementation quality and market conditions.

What’s the difference between AI agents and regular AI chatbots?

A standard chatbot responds to individual prompts with single answers. An AI agent system receives a goal, autonomously breaks it into subtasks, creates multiple specialized workers, and executes them in parallel — including self-correction, research, and multi-tool coordination. My 100-agent setup runs across different software platforms simultaneously, with each agent using whichever AI model performs best for its specific task.

Final Thoughts: The System Matters More Than the Hype

I’ve been in this space since May 2023, when I launched Turkey’s first AI training course. Over 3,000 people enrolled within the first day. I’ve since created hundreds of thousands of pieces of content and trained countless people to build their own businesses.

What I’ve learned: the people who succeed aren’t the ones chasing every new AI tool. They’re the ones who build systems. One well-designed agent with the right models, skills, and feedback loops outperforms ten thousand tools tested randomly.

Start with the foundation. Connect your agents properly. Choose your models based on actual task performance, not hype. And most importantly — build toward revenue, not just impressive demos. The technology is real. The results are real. But only if you treat it as system design, not magic.

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 Ran 100 AI Agents for 10 Minutes — Here’s What Happened appeared first on Digital Market Mentoring.

Key Takeaways

• 1 million token context window lets Minimax M3 hold entire codebases, books, or large projects in memory without forgetting details
• 79% score on software testing benchmarks, surpassing GPT-5.5 and Claude 3.1 Pro — despite being nearly free
• Desktop agent capability distinguishes it from chatbots: it sees your screen, controls your computer, and works autonomously
• Multimodal understanding of text, images, video, and live screen capture in a single model
• Cost is “pocket money” compared to competitors — per-million-token pricing in the cents range
• I tested it head-to-head against Claude Opus 4.8 and Codex 5.5 on identical game-building prompts

Why Everyone Is Talking About Minimax M3

Three capabilities converged in this release for the first time, and I believe that’s why it’s causing this level of disruption.

First, the 1 million token context window. I can feed it an entire project — a full book manuscript, a massive code repository — and it retains everything. Nothing gets lost in the middle. For the kind of complex automation work I do in e-commerce and online business, this changes what’s possible.

Second, it’s genuinely multimodal. It doesn’t just process text. It sees images, understands video, and critically — it sees your computer screen in real-time. This isn’t theoretical. When I ran my tests, the agent was literally watching my desktop and making decisions based on what it observed.

Third, the pricing. While competitors charge amounts that make serious automation expensive at scale, Minimax M3 is priced at what I’d call pocket money. Per million tokens, we’re talking cents. There’s even a free tier to start testing the desktop agent. I honestly couldn’t find an excuse not to try it.

The benchmark that stopped me cold: 79% on software testing. That’s higher than GPT-5.5 and Claude 3.1 Pro. An open-source Chinese model beating the most expensive American models on coding tasks — this would have seemed impossible a few years ago.

Setting Up My First AI Employee

Here’s where Minimax M3 diverges from every chatbot I’ve used. It has a desktop application. You install it on your computer, and it becomes what the company calls a “personal assistant” or what I configured — using the Hermes Agent framework — as an autonomous worker.

The fundamental difference from other AIs: it doesn’t just talk. It does the work. The backend contains thousands of capabilities. I connected my files, my email, my calendar. Now it works with my actual data, my actual world — not generic training data.

I gave my agent a specific goal: generate £1 million in sales, and don’t stop until achieved. It will continuously improve itself and scale operations. You can program any life goal this way — write the plan, develop the capability, and the AI works daily toward it.

The configuration requires precision. You define exactly what the agent does, provide detailed descriptions, and specify boundaries — what it should do, what it must never do. You choose between asking permission for each step or granting full autonomous operation. I tested both modes.

Remote control works through Telegram bot, WeChat bot, or Lark. I can monitor and direct my desktop agent from my phone, 24/7, even when I’m away from my computer.

Test 1: Building a Professional Website in Under a Minute

I wrote a single prompt: build me a beautiful gaming website, with all files contained in a single HTML file. I selected Minimax M3 and started the system.

The result loaded in seconds. A visually polished website with working navigation, pricing sections, FAQ — all functional, all professional. I’ve paid thousands of pounds for worse work on freelance platforms. The kind of output that local businesses currently purchase at premium rates.

I ran a parallel test requesting an “Ocean” themed website. Again, Minimax M3 produced something I found genuinely impressive — arguably more visually refined than my own site. The buttons worked. The redirects functioned. The pricing and contact sections rendered correctly.

Test 2: The Three-Way Game Build-Off

This is where I pushed all three models equally hard. Same prompt, same complexity, simultaneous execution.

The prompt: Create a playable HTML game similar to Mario. Catch falling gold coins, avoid bombs. Single HTML file. Start screen, scoreboard, three lives, modern design with animations, sound effects. When finished, create the file and open it so I can play immediately.

I ran Minimax M3 on the left, Claude Opus 4.8 in the center, and Codex 5.5 on the right. All received identical instructions. I pressed start simultaneously.

Claude finished first. The game loaded in browser. Space to start. The physics felt deliberately challenging — I found the platforming genuinely difficult, perhaps overly so. The controls responded, the character moved, but completing the level appeared nearly impossible in my attempts. Solid execution from a single prompt, but punishing difficulty.

Codex completed second. Simpler controls, more basic visual presentation. Functional but less ambitious. I could play it, the mechanics worked, but it felt like a minimum viable product compared to what I’d hoped for.

Minimax M3’s result surprised me most. Visually, it was clearly superior — the graphics, the polish, the overall presentation stood apart. It included lava hazards alongside the requested bombs and coins. The scoreboard functioned. Three lives displayed. Level progression worked. The spacebar controlled jumping. When I hit a bomb, I lost a life — the system logic operated correctly.

Playing it revealed intentional difficulty. The obstacles were dense, the timing tight. But the completeness of the package — sound effects, animations, visual design, functional game loop — from a single sentence prompt, without me writing a line of code?

I concluded that Minimax M3 produced the strongest visual result by a clear margin. The other two models created playable games, but Minimax M3’s output looked like something you’d consider for actual App Store release — with integrated Google Ads, potential for download revenue, the complete commercial pipeline.

What This Means for Online Business

I’ve been automating e-commerce operations for years. This feels different.

The agent framework means I can describe a person’s entire job — research trending products, compile lists, email reports, update inventory — schedule it, and the system executes daily without me. The human becomes the prompt writer and supervisor, not the doer.

I tested this specifically: I had the agent research currently trending products by country for eBay, generate the list, and email it to me. The entire workflow that previously required paid software subscriptions completed in approximately one minute, autonomously.

For my own trading operations, I can now deploy agents that work continuously, use the cheapest available tokens efficiently, and scale without linear labor costs. The Hermes Agent integration I configured optimizes this further — routing tasks to the most cost-effective model while maintaining quality.

Current Availability and Pricing

At launch, Minimax M3 is running a 50% promotional discount for 7 days. The per-million-token pricing sits in the cents range — I verified this against my own usage. The desktop agent has a free starting tier for testing.

The model weights are openly available. Someone without significant budget can run this on their own hardware. The barrier to entry for sophisticated AI automation has effectively disappeared.

FAQ

What exactly is Minimax M3?

Minimax M3 is an open-source large language model developed in China, released in June 2025. It features a 1 million token context window, multimodal capabilities (text, image, video, screen understanding), and a unique desktop agent application that can autonomously control a computer to perform tasks.

How does Minimax M3 compare to GPT-5.5 and Claude for coding?

On software testing benchmarks, Minimax M3 scored 79%, surpassing both GPT-5.5 and Claude 3.1 Pro. In my direct testing with identical game-building prompts, Minimax M3 produced more visually complete and polished results, though Claude finished faster and Codex produced simpler but functional output.

Is Minimax M3 actually free to use?

The model weights are open-source and free to self-host. The API pricing is significantly cheaper than competitors — per-million-token costs are in cents rather than dollars. There’s a free tier for testing the desktop agent, with paid tiers for higher usage. A 50% launch discount was active at time of testing.

Can Minimax M3 really replace human workers?

It can automate many repetitive computer-based tasks — research, data compilation, website building, code generation, report creation. However, it requires precise prompt engineering, clear boundary-setting, and human supervision. I view it as amplifying individual productivity rather than full replacement, though the economics of certain roles will shift dramatically.

Final Thoughts

I went into this test skeptical of the hype around another “GPT killer.” I finished it convinced that Minimax M3 represents something specific and valuable: the convergence of capable coding, genuine multimodal understanding, and autonomous action at a price point that removes financial barriers.

The desktop agent isn’t a demo. It’s a working tool I now have running scheduled tasks for my business. The website and game builds weren’t cherry-picked — they were first attempts with single prompts.

For entrepreneurs building online businesses, especially in e-commerce and automation, my recommendation is straightforward: test the free tier. Configure one agent with one specific task. Watch it work while you’re doing something else.

The first task I delegated was product research. I’m now deciding what to automate next.

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 Tested Minimax M3: The AI That Took Over My Computer appeared first on Digital Market Mentoring.

Key Takeaways

• Akio is not a chatbot—it’s a team of AI agents (market analyst, product researcher, supplier marketer, store designer, marketer) that collaborate autonomously
• Trained on Alibaba’s real sales data, not internet scraping—so product recommendations use actual market performance, not guesses
• I built a functional Shopify store in ~30 minutes with product research, supplier matching, and listing creation automated
• Built-in plugins replace separate tools for SEO, email marketing, social media, and multi-platform selling (eBay, Amazon, Shopify)
• Full automation is possible, but beginners should use “default permission” mode to review actions before approval
• 14-day free trial available; desktop application required

What Akio Actually Is (And Why It’s Different)

Let’s be clear: this isn’t another ChatGPT wrapper. Akio is Alibaba’s AI agent team—multiple specialized agents working together. When you enter a goal, they divide the work among themselves: the market analyst checks demand, the product researcher finds opportunities, the supplier marketer connects you with wholesalers, the store designer builds your storefront, and the marketer handles promotion.

The critical difference? Akio doesn’t guess from internet data. It’s trained on Alibaba’s actual platform data—1 billion products, 50 million suppliers. When I asked what sells well in Germany, it didn’t hallucinate based on blog posts. It pulled from real transaction data. That’s the gap between this and generic AI tools.

Setting Up: My Live Test from Zero

I approached this as a complete beginner would. Downloaded the desktop app, created a free account (14-day trial, no upfront risk), and selected the Shopify agent from the available options (Shopify, Amazon, Gmail, and others).

Before starting, I checked three important settings:

• Plugins: Activating the Global Seller Agent plugin unlocks deeper brand and supplier details
• LLM model: I left it on “auto” so it selects the best model for each task
• Permissions: Default permission for beginners (reviews actions before executing), or full access for hands-off automation—I used full access for this test

My first prompt was intentionally naive: “I’m new to Shopify. I want to sell products. What should I sell? Walk me through this step by step.”

The 30-Minute Store Build: What Actually Happened

The agent immediately created a plan on the left panel and started asking me questions. It suggested the pet niche—specifically household pets. I accepted its recommendation. Within seconds, it was analyzing Shopify stores, Halimaba inventory, and global market data.

Then came the precision. It identified “Magic Organ Cat” and a cat umbrella as specific products with proven demand. For beginners who don’t know what will sell, this eliminates hundreds of trial-and-error tests and thousands in wasted ad spend.

I pushed further: “If you were me, which would you choose with low capital?” It recommended the cat toy set—high demand, low risk, easy packaging. Then it found the supplier automatically.

For my second, more advanced prompt, I specified: “Find products under $5 cost, high demand in US and UK, home accessories and tools, reliable supplier, build my Shopify store.” This is really all the input needed. The agent researched, found suppliers, and prepared listings while I could have gone for a walk.

Results appeared in seconds. One product: $11 retail price, projected $11,000 monthly revenue. I verified this wasn’t fiction—the same product already had 4,000+ sales on Amazon with strong ratings. Real market validation, not AI hallucination.

The Hidden Power: Plugins and Multi-Platform Control

Here’s what most reviews miss. Akio’s plugin ecosystem essentially replaces your entire software stack. I’m talking:

• WordPress integration
• Shopify SEO tools
• Email marketing platforms
• Social media management
• eBay store automation

Connect your eBay store, and the AI becomes your assistant—automatically responding to customers, managing listings, even handling account health. It specifically monitors for MC011 and MC999 notices (eBay’s restriction warnings) and flags when seller performance drops below standard. These are seller-killing issues that normally require constant manual vigilance.

For dropshippers, this means product research software, listing tools, and inventory managers become redundant. The sellers who integrate this system will simply outpace those still using 5-10 separate tools.

Skills, Scheduling, and Daily Automation

The “Skills” section lets you add capabilities or create custom ones. I loaded Amazon Market Research—instantly replacing paid product research software. It finds viral products in seconds using world-class AI analysis.

Products from $16.88, suppliers connected instantly. But it’s not just for dropshipping or FBM sellers.

In the “Receivers” section, I connected Gmail, Twitter, LinkedIn, and Instagram. Now I manage everything from one brain. I tell it: “Post on Instagram”—it posts. “Check my emails”—it responds. “Update LinkedIn”—done. No more switching between 100 different tools.

The Schedule Task section is where long-term automation lives. My setup includes: startup preparation, end-of-day reports, email monitoring, computer checks, trend discovery, and news alerts. You can assign specific days to each agent—my copywriting agent, product research agent, and Shopify monitoring agent each work on schedules I set.

Mobile Control: Running Your Business from Telegram

In the Channels section, I connected Telegram and Discord bots. Here’s what this actually means in practice: I get a Telegram message—“Akin, I found a trending product. Should I list it?” I reply: “Yes, optimize SEO, add photos, list with good margin. Create a post, run a $10-15 daily test campaign, 5 variations.”

Tasks that used to take me dozens of hours now happen in 10 minutes with voice messages. The system executes while I live my life.

What Nobody’s Saying: The Honest Caveat

I’ve been building online businesses for years. I’ve used hundreds of tools. Akio is genuinely different—but let me be direct about what matters.

This doesn’t guarantee income. Dropshipping isn’t dead, but the methods have changed. The sellers who adapt to AI agents will outperform those who don’t. The sellers who treat this as “set and forget” without understanding their numbers will still fail.

The 14-day trial lets you test without risk. My recommendation: start with default permission mode, review what the AI does, understand the logic, then gradually enable full automation. The tool is powerful enough to build your store, but you still need business judgment.

Alibaba is bringing this to London soon—I’ll be at their event presenting a $1 million system project I’ve developed. If you’re London-based and building in this space, it’s worth connecting.

FAQ

Is Akio free to use?

Akio offers a 14-day free trial with full feature access. After that, paid plans apply. You need to download the desktop application to use it.

Can Akio really replace my existing dropshipping tools?

Based on my testing, Akio’s integrated product research, supplier matching, listing creation, and multi-platform plugins can replace separate tools for product research, inventory management, SEO, email marketing, and social media scheduling. However, you’ll still need your own Shopify/eBay accounts and payment processing.

How does Akio’s product data differ from ChatGPT?

ChatGPT scrapes and synthesizes internet information, which may be outdated or inaccurate. Akio is trained directly on Alibaba’s platform data—1 billion product listings and 50 million supplier records with actual sales performance, not estimates.

Is full automation safe for beginners?

I recommend beginners use “default permission” mode, which requires you to review and approve each action before execution. Full access mode lets the AI operate autonomously, which is powerful but requires you to understand what’s happening to catch any errors.

Final Thoughts

I went into this test skeptical. Another AI tool promising to build my business? I’ve heard that before. But watching Akio research real products with verified sales data, match me with suppliers, and prepare a functional store in 30 minutes—while I did nothing—changed my assessment.

This isn’t about getting rich overnight. It’s about the compounding advantage of automation: the hours saved on research, the elimination of guesswork, the ability to manage multiple channels from one system. The dropshippers who integrate AI agents into their workflow will operate at a scale and speed that manual operators simply can’t match.

The strategy has changed. Adaptation isn’t optional anymore.

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 Let Alibaba’s AI Build a Dropshipping Store—Here’s What Happened appeared first on Digital Market Mentoring.