Key Takeaways

• Bingöl University was overflowing — attendees filled the stairs and every available space
• Students and speakers shared domain-specific knowledge that created immediate practical value
• The organizer’s energy and team coordination made the event feel seamless despite the crowd size
• Multiple sponsors participated, suggesting local businesses see university events as viable marketing channels
• Every speaker I interviewed emphasized the same thing: young people’s energy is “nourishing” for experienced entrepreneurs

Why I Said Yes to a University Event in Eastern Turkey

I’m based in the UK. My businesses run on automation, AI tools, and e-commerce platforms. Most of my networking happens through LinkedIn, Twitter, or Zoom calls. So when the invitation came to speak at Bingöl University, my first instinct was to check my calendar for conflicts — not excitement.

But I’ve learned something after 15+ years of entrepreneurship: the opportunities you almost decline often become the most valuable. I accepted, flew to Turkey, and drove to Bingöl. The city center has that restaurant I mentioned — the one with the puzzling name. I walked past it twice trying to decode the marketing logic. Sometimes bad branding is just bad branding. No deeper lesson there.

What struck me immediately was the organizer’s humility. She told me she hadn’t expected this level of interest. The university hall was genuinely overflowing. I saw people sitting on staircases, standing in doorways. In an age where online webinars struggle to hold 40% attendance rates, this physical space had demand exceeding supply.

What Happens When 500+ People Share Knowledge in One Room

I interviewed attendees throughout the day. One student told me the event was “very beautiful, very enjoyable, very entertaining.” Another said it was “a different kind of organization” and hoped to see more like it. The specificity of their feedback mattered more than the compliments.

Here’s what I observed: when everyone shares knowledge from their own domain, cross-pollination happens naturally. A marketing student hears an AI automation case study. A computer science major listens to an e-commerce founder explain customer acquisition costs. These intersections don’t happen efficiently online because algorithms feed us more of what we already consume.

The organizer’s team executed something I’ve struggled with in my own virtual events: maintaining energy across a full day program. One team member told me she was “incredibly happy” to witness her sister’s effort firsthand. Another said serving as a sponsor representative felt meaningful because she could see the impact directly. There’s a accountability in physical events that disappears when everyone is muted on a call.

The Sponsorship Model Universities Are Getting Right

I spoke with multiple sponsors at the event. One representative described the experience as “very beautiful, very enjoyable, a magnificent organization.” What interested me was the sponsorship structure — it wasn’t transactional logo placement. The sponsors were integrated into the event flow, speaking with attendees, collecting feedback in real-time.

For online business owners reading this: university events in secondary cities represent undervalued sponsorship opportunities. The cost per genuine interaction is often lower than digital advertising in saturated markets. Bingöl isn’t Istanbul or London. Competition for attention is lower, but engagement depth is higher because attendees have fewer competing events.

I noticed the sponsor representative I interviewed was also a volunteer — her sister was the main organizer. This blending of personal investment and professional representation created authenticity that no influencer contract can manufacture. I’m considering how to replicate this dynamic in my own affiliate and partnership programs.

What Young Entrepreneurs Taught Me About Energy Management

The most surprising feedback came from experienced speakers, not students. Multiple speakers told me the same thing: “The energy of these young people nourishes me, and I try to nourish them in return.” One speaker said explicitly: “Our goal is to give a message to these young people, we have to do our duty.”

This reversed my assumption about speaking events. I thought experienced entrepreneurs gave value and students received it. The reality was mutual. After months of running my UK businesses through screens, being in a room where ambition was physically palpable — where I could see facial expressions, hear spontaneous laughter, feel the collective attention shift — reminded me why I started.

Another speaker told me: “I’m very excited. On behalf of the team, I’m very happy. Because we’re all inside such an event together.” The “together” part matters. I’ve built communities of 10,000+ online. The dopamine metrics look impressive. But I can’t honestly say they create the same psychological commitment as sharing physical space for six hours.

The Honest Truth About ROI from Offline Events

I won’t pretend this trip generated immediate revenue. I didn’t land a client. I didn’t sell a course. The direct ROI was negative when you factor in flights, time away from my UK operations, and the opportunity cost.

But here’s what did happen: I collected video testimonials I’ll use for years. I met a potential technology partner for my AI automation projects. I understood a market segment — Turkish university students interested in entrepreneurship — that my analytics had never properly revealed. One student asked me a question about e-commerce logistics that exposed a gap in my standard presentation. I’ll revise my entire talk because of that 90-second interaction.

The return on offline events compounds slowly and unpredictably, which makes it hard to justify in quarterly planning. But my most important business relationships — my current CTO, my longest-running affiliate partner, my investor — all began at events where the immediate value was unclear.

FAQ

How do you get invited to speak at universities?

I received this invitation through my existing network in Turkey, but I’ve also cold-pitched university entrepreneurship centers using specific case studies from my businesses. The key is demonstrating relevant expertise with concrete numbers, not general motivational content.

Are university events worth the time for established entrepreneurs?

Not always. I evaluate based on audience specificity, organizer track record, and whether the location exposes me to a network I can’t reach digitally. Bingöl University made sense because I was expanding into Turkish-speaking markets.

How can online business owners replicate offline connection?

You can’t fully replicate it, but you can improve digital interaction through structured video calls, small group formats under 15 people, and hybrid events with local meetup components. I now require at least one in-person team gathering quarterly.

What should speakers prepare for university audiences in 2024?

Students are skeptical of generic success stories. I prepare specific failures — the restaurant I mentioned with bad branding, a failed product launch, an automation that cost more than it saved. Authenticity outperforms inspiration in every audience feedback form I’ve collected.

Conclusion

I flew back to London with that restaurant name still puzzling me. But the real takeaway was clearer: I’ve been over-optimizing for scalable digital interaction and under-investing in inefficient, unmeasurable, irreplaceable human presence. The Bingöl University event didn’t fit my standard metrics. It was messy, crowded, logistically complicated. People sat on stairs. The microphone cut out twice. A student asked me a question I couldn’t answer well.

And it was one of the most useful business experiences I’ve had this year. I’m already reviewing my 2024 calendar to block time for three more events — not because I can prove the ROI, but because I’ve finally accepted that some value only emerges when you’re physically present to receive it. The algorithm can’t serve you that.

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 Spoke at Bingöl University — Why Offline Events Still Matter in 2024 appeared first on Digital Market Mentoring.

• AI enables creators to transform one video into 30+ diverse content pieces, boosting efficiency and audience reach.
• Leverage AI transcription and summarization to extract text for blogs, emails, and social media from your video.
• Utilize AI video editing tools to automatically generate short, platform-specific clips and expand your content distribution.

Repurposing one video into 30 pieces of content with AI is highly achievable by leveraging artificial intelligence to transcribe, summarize, translate, and reformat your original video’s core message. This strategy allows creators to extract various short-form videos, blog posts, social media updates, and audio snippets from a single long-form asset, significantly extending its reach and value across multiple platforms.

As an e-commerce entrepreneur and content creator, I’ve seen firsthand how crucial efficiency is. In 2026, the landscape of online content is more competitive than ever, and simply creating one great video isn’t enough. You need to make that video work harder for you. This is where AI becomes an indispensable partner, transforming a single effort into a content powerhouse that feeds your social media, blog, and email marketing for weeks.

Why is Video Repurposing Essential for E-commerce & AI Entrepreneurs?

For anyone building an online income stream, whether through eBay dropshipping, e-commerce stores, or AI-powered ventures, consistency in content creation is key to audience growth and engagement. However, the demands of running a business often leave little time for daily content production. Repurposing solves this challenge by enabling you to maintain a strong content calendar without constant new ideation and filming. It ensures your message reaches different segments of your audience who prefer various content formats—some prefer reading, others watching short clips, and still others listening to podcasts. By diversifying your content, you increase touchpoints and reinforce your brand’s authority and presence across the digital ecosystem. Moreover, it’s a smart way to get more mileage out of your research and expertise, making every minute spent creating that original video incredibly valuable.

How Can AI Transcribe and Summarize Your Video Content?

The first step in any effective repurposing strategy is to get a text version of your video. AI transcription tools are incredibly accurate in 2026, converting spoken words into written text within minutes. Services like OpenAI’s Whisper API, or integrated features within video editing suites, can handle this with impressive precision. Once you have the transcript, the real magic begins with AI summarization. Large Language Models (LLMs) can take that lengthy transcript and distill it into various formats:

• Executive Summaries: Perfect for email newsletters or quick blog intros.
• Key Bullet Points: Ideal for social media carousels or presentation slides.
• Short Paragraphs: Use these for Instagram captions or LinkedIn posts.
• Tweet-sized Snippets: Generate multiple distinct tweets from different sections of your video.

This process saves hours of manual work, allowing you to quickly identify the most impactful quotes, statistics, and insights from your video. I often feed the full transcript into an LLM with prompts like, “Extract 10 unique, compelling quotes from this text that can stand alone as social media posts,” or “Summarize the main arguments of this video into a 200-word blog post.” The results are almost always ready for minor edits and immediate deployment.

What are the Best AI Tools for Extracting Short-Form Video Clips?

Turning a long video into dozens of short, engaging clips is where AI truly shines for visual content. Tools designed for video editing and content repurposing, often powered by AI, can identify ‘hook’ moments, key discussion points, and impactful statements within your original footage. They can automatically:

1. Detect Speakers and Topics: AI can analyze who is speaking and what they’re discussing to segment your video.
2. Identify Engagement Spikes: Some AI tools can even detect moments of higher energy or emphasis, suggesting these as prime candidates for short clips.
3. Auto-Generate Captions & Subtitles: Essential for social media, where many users watch without sound.
4. Reformat for Different Platforms: Automatically adjust aspect ratios (e.g., 9:16 for TikTok/Reels, 1:1 for Instagram feed) and add visual elements like progress bars or animated text.

For example, if I have a 30-minute tutorial on ‘eBay dropshipping best practices,’ I can use an AI video editor to find all instances where I discuss ‘product research,’ ‘listing optimization,’ or ‘customer service tips.’ It will then cut these into 30-60 second standalone clips, ready for platforms like YouTube Shorts, TikTok, and Instagram Reels. This not only saves immense time but also ensures that each short piece of content is targeted and impactful. Remember, always check eBay’s official policies regarding listing practices and dropshipping to ensure compliance: eBay Dropshipping Policy.

How Can One Video Fuel Your Blog, Email, and Podcast Strategy?

Beyond visual and textual snippets, your single video is a goldmine for long-form content across other mediums. Here’s a breakdown of how AI can help:

Blog Content

The full transcript, enhanced with AI summaries and expansions, can easily become a comprehensive blog post. You can ask an LLM to:

This approach transforms a spoken explanation into a readable, searchable article that can rank on search engines and drive organic traffic.

Email Marketing

From the same video, you can derive:

• Newsletter Segments: Short, punchy summaries or key takeaways from the video.
• Email Sequences: A series of emails, each expanding on a different point made in the video, leading back to the full video or a related product.
• Promotional Emails: Announcing the new video, with compelling snippets and a clear call to action to watch.

Podcast Episodes

The audio track of your video is, in essence, a podcast episode. With minimal editing, you can publish it directly to podcast platforms. Furthermore, AI can help you:

• Generate Show Notes: Summarize the episode and list key timestamps.
• Create Episode Titles & Descriptions: Optimize for podcast SEO.
• Extract Audio Clips: Short audio snippets for social media promotion of the full episode.

By systematically applying these AI-powered strategies, one comprehensive video can genuinely become the foundation for dozens of content pieces, ensuring your message permeates every corner of your audience’s digital world. This is not just about saving time; it’s about smart, strategic content distribution that amplifies your efforts and ultimately contributes to your online income goals.

Ready to unlock the full potential of your video content? Start experimenting with AI tools today and watch your content output multiply. This educational content may contain information about third-party tools or services. Please perform your own due diligence before making any purchases or commitments.

Frequently Asked Questions

How many pieces of content can one video be repurposed into with AI?

With AI, a single video can realistically be repurposed into 30 or more distinct pieces of content. This includes short-form video clips, blog posts, social media updates, email snippets, and audio excerpts, maximizing its utility across various platforms and formats.

What AI tools are best for video transcription?

Leading AI tools for video transcription in 2026 include services based on OpenAI’s Whisper API, as well as integrated transcription features within popular video editing software. These tools offer high accuracy and speed, converting spoken words into text effortlessly.

Can AI automatically create short video clips from a long video?

Yes, AI can automatically create short video clips from a longer video. Specialized AI video editing tools can identify key moments, segment discussions, and reformat clips for different platforms like TikTok or Instagram Reels, significantly streamlining the content creation process.

How can AI help turn a video into a blog post?

AI can transform a video into a blog post by first transcribing the video’s audio into text. Then, large language models can summarize, expand, and structure this transcript into a coherent, SEO-optimized blog post with introductions, headings, and conclusions, saving considerable writing time.

Legal and Affiliate Disclosure

This content is for education and general information only; it is not legal, financial, tax or professional advice and does not guarantee income, sales, approval or results. Some links may be affiliate links; I may earn a commission at no extra cost to you.

The post Repurpose One Video into 30+ Content Pieces with AI appeared first on Digital Market Mentoring.

Introduction

Akin Yilmaz, the London operator behind digitalmarketmentoring.com, has been at the forefront of AI systems, ecommerce automation, and content systems. In this article, we delve into the recent unveiling of Anthropic’s Hidden AI and the immediate testing conducted by Claude Fable. This comprehensive review aims to provide insights into the AI’s capabilities and its real-world application.

Key Takeaways

• The Hidden AI by Anthropic has been a subject of intrigue within the AI community.
• Claude Fable’s testing offers a glimpse into the AI’s performance and potential.
• The implications of such AI advancements are far-reaching, affecting various industries.

What is Anthropic’s Hidden AI?

Anthropic, a company at the forefront of AI research, has recently unveiled their Hidden AI, a model that has been kept under wraps until now. This AI model is designed to offer advanced capabilities, and its release has sparked interest among tech enthusiasts and professionals alike.

Why Test the AI Immediately?

The urgency to test the AI immediately stems from the desire to understand its real-world applications and implications. By conducting tests, we can gauge the AI’s performance, identify its strengths and weaknesses, and foresee how it might shape the future of AI technology.

Testing Results and Analysis

In the video titled “Anthropic’s Hidden AI Finally Came Out — I Tested It Immediately (Claude Fable)”, we learn about the immediate testing conducted by Claude Fable. The testing process involved a series of tasks and challenges to evaluate the AI’s capabilities.

Performance Metrics

1. Accuracy: The AI’s ability to provide correct and precise responses was one of the primary metrics assessed.
2. Speed: How quickly the AI could process information and deliver results was also a critical factor.
3. Adaptability: The AI’s capacity to adapt to new tasks and learn from interactions was another important aspect.

Implications and Applications

The results of the testing have significant implications for various industries. From ecommerce automation to content systems, the capabilities of Anthropic’s Hidden AI could revolutionize the way businesses operate and interact with their customers.

Frequently Asked Questions

What is the significance of Anthropic’s Hidden AI?

The significance lies in its potential to advance AI technology and its applications across different sectors, offering more efficient and intelligent solutions.

How does the Hidden AI compare to other AI models?

The Hidden AI stands out due to its unique capabilities and performance metrics, which are showcased through rigorous testing and real-world applications.

What are the potential uses of such AI technology?

The potential uses are vast, ranging from improving customer service in ecommerce to automating complex tasks in various industries.

How can businesses leverage this AI technology?

Businesses can leverage this AI technology by integrating it into their systems to enhance efficiency, improve decision-making, and offer personalized experiences to their customers.

Conclusion

The unveiling of Anthropic’s Hidden AI and the subsequent testing by Claude Fable provide valuable insights into the future of AI technology. As we continue to explore and understand the capabilities of such AI models, the potential for innovation and transformation across industries becomes increasingly apparent.

For more in-depth knowledge and guidance on leveraging AI in your business, consider joining the Skool community, downloading the free starter PDF, or booking a call with Akin Yilmaz. For those ready to take the next step, apply for the Inner Circle or explore TurkoLister for your ecommerce needs. Watch the full transmission on YouTube: https://www.youtube.com/watch?v=kTx5mbp1WlA

Explore More

Dive deeper into the world of AI and digital marketing with Akin Yilmaz’s full channel.

Watch the full transmission: https://www.youtube.com/watch?v=kTx5mbp1WlA

The post How Anthropic’s Hidden AI Performed in Real-World Testing – A Comprehensive Review 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 nobody in Turkey was talking about when I first discovered this — and what I learned after actually using it for my e-commerce automation business.

Key Takeaways

• DeepSeek V4 Pro scores 3,006 on coding benchmarks vs. ChatGPT-4o’s 3,068 — a 0.2% difference despite being 35x cheaper
• I reduced my total AI spending from ~$420/month to $41-42/month — saving roughly $3,000+ per year
• 1 million token context window vs. ChatGPT’s 128K (8x smaller) and Claude’s 200K (5x smaller)
• Free “Cloud Code” alternative exists with identical interface but DeepSeek V4 running in the backend
• Four real risks: data goes to Chinese servers, slower access from Turkey, no image support, banned in some EU countries
• Open-source means you are responsible — no customer service, no refunds, no uptime guarantees

What DeepSeek V4 Actually Is (And Why the Benchmarks Shocked Me)

DeepSeek V4 launched with two versions: Pro for deep work and complex coding, and Flash for daily tasks. Both are open-source and available free on Hugging Face with no usage limits. When I first saw the numbers, I literally double-checked my translation — I thought I was misreading something.

On Codeforces-style benchmarks (the world’s highest-level competitive coding platform), DeepSeek V4 Pro scores 3,006. ChatGPT-4o scores 3,068. The gap? 0.2%. For Claude Opus, you’re looking at roughly $15 per million tokens. DeepSeek V4 Pro? $1.74 per million tokens. ChatGPT comes in around $5 per million. I ran the math three times because it felt impossible.

When I checked Reddit’s largest open-source AI forum, a post titled “DeepSeek V4 could kill Claude by year-end” had 2,000 upvotes and 1,000 comments. Developers were sharing the same experience I was having: one wrote “Worked with it for a week. Don’t want to go back to ChatGPT. Same quality, one-tenth the price.” Another discovered it speaks Turkish fluently. A third reported $200 in monthly savings by switching their Claude Code workflow to DeepSeek.

The 1 Million Token Context Window: What It Actually Means

Here’s where DeepSeek V4 genuinely separates itself. While everyone else talks about context windows in abstract numbers, I want to tell you what this enabled for my actual business:

I fed it a 200-page customs regulation document — it read the entire thing. I gave it a 700-page e-commerce database — it analyzed the complete structure. I dumped an entire website’s content into it — it evaluated everything in a single pass. ChatGPT and Claude simply cannot do this. The context window isn’t a spec-sheet bragging point; it’s a fundamentally different capability that changes what AI assistance means for complex business analysis.

Meanwhile, Claude has started introducing restrictions that pushed me away. Even on their Pro tier at $17, I found I couldn’t use certain features or complete normal tasks due to new limitations. The value proposition was eroding before I even found the alternative.

How I Built a Free Claude Code Alternative (15-Minute Setup)

This was my biggest discovery this week — and as far as I can tell, nobody in Turkey knows about it yet. There’s an open-source project called Free Claude Code that replicates Claude’s interface identically. Same commands, same workflow, same feel. But behind the scenes? DeepSeek V4 is running instead of Claude.

Here’s what this means practically: that £200/month interface you might be paying for? The system gives you functionally the same experience for free. Setup took me 15 minutes. You copy the repository, configure it, and you’re running. I shared the complete Turkish setup guide with my community, including download links and step-by-step instructions.

The process: create your API key in the operator panel, choose which DeepSeek V4 variant you want (Pro or Flash), and connect. For security-conscious work, I run everything through a VPS server — costs about $6/month, keeps your data off Chinese servers, and gives you 24/7 uptime for automation agents.

The Four Risks Nobody Talks About

I respect my audience enough to tell you the downsides that hype videos skip. Here are the four real risks I identified:

1. Data Policy: Your Information Goes to Chinese Servers

DeepSeek stores data on Chinese servers. If you’re handling sensitive business information, do not send it through their API directly. Download the model from Hugging Face, run it locally, or host it on a VPS with providers like Bluehost. For $6/month, your data stays yours.

2. Speed: Slower Access from Turkey

From Turkey, DeepSeek is roughly 400 milliseconds slower than ChatGPT. For most tasks, this is imperceptible. If speed is critical, the VPS approach I mentioned eliminates this issue entirely.

3. No Image Support

V4 is text-only. For visual work, I still use ChatGPT or Gemini — though I’ve reduced this to about one hour per week, roughly $20/month. For everything else, DeepSeek handles it.

4. Regulatory Bans

DeepSeek is banned in Germany and faces restrictions in several other countries. As of my testing, the UK has no technical issues, but this landscape changes quickly.

My Actual Cost Breakdown: From $420 to $41/Month

Let me be completely transparent about my current setup, because I know readers want real numbers, not theoretical savings:

• eBay research software I built: DeepSeek V4 Flash handles product identification with pinpoint accuracy — $5/month
• Coding, deep analysis, cold email automation, other workflows: DeepSeek V4 Pro — $10/month total
• Visual/image tasks: ChatGPT/Gemini, roughly one hour weekly — $20/month
• VPS server for secure local hosting and 24/7 agents — $6/month

Total: $41-42/month

I was spending approximately $279/month previously on AI tools alone. That’s over £3,000 per year — nearly what two children’s annual school fees cost in Turkey. The savings are real, but only if you’re willing to take on the responsibility of managing open-source infrastructure.

The Hard Truth About Open Source: You’re On Your Own

I need to be direct here, because I see too many creators presenting open-source AI as “free money.” It’s not. Open source means you are responsible. There’s a critical difference between “free” and “open source.”

When something breaks with ChatGPT, you open a ticket. When Claude goes down, you get status updates. With DeepSeek? ChatGPT doesn’t call you to explain what went wrong. Claude doesn’t issue refunds if your data disappears. No company sends apology emails when servers crash.

You are the system administrator now. But here’s what I’ll tell you: the first 100 Turkish entrepreneurs who accept this responsibility will be 10 years ahead of everyone else. Because the person who learns to connect APIs, troubleshoot errors, and continuously improve their automation stack doesn’t just save money — they build a skill that compounds.

I’ve watched this pattern across e-commerce platforms, sales channels, and AI tools. Dirksik, Alta, Eskif — platforms come and go. But the capability you build? That stays with you. When you learn these skills, you adapt to new tools 10x faster than competitors who never started. The people who never begin? They’re already behind.

FAQ

Is DeepSeek V4 really as good as ChatGPT-4o and Claude?

For coding and text analysis, the benchmark gap is 0.2% versus Claude (3,006 vs. 3,068). For most business automation, content analysis, and development tasks, I found it functionally equivalent. For image generation and certain creative tasks, ChatGPT and Gemini still lead.

How do I keep my data secure with a Chinese AI model?

Don’t send sensitive data through DeepSeek’s API directly. Download the open-source weights from Hugging Face and run locally, or host on a VPS (~$6/month) with a non-Chinese provider. This keeps your business data under your control.

Can I really set up the free Claude Code alternative in 15 minutes?

Yes, if you’re comfortable with basic command-line operations. The “Free Claude Code” project clones Claude’s interface exactly. You copy the repository, add your DeepSeek API key, and configure which model variant to use. I documented the full Turkish-language setup in my community.

What happens if DeepSeek gets banned or shut down?

This is the open-source risk. Unlike paid services with SLAs, there’s no guarantee of continuity. However, because the model weights are downloadable, you can continue running existing versions locally even if API access disappears. I maintain ChatGPT and Claude as backups for critical workflows — I use them roughly one hour weekly.

Conclusion

DeepSeek V4 didn’t “kill” ChatGPT or Claude for me. What it did was make them unnecessary for 90% of my work. I now run one primary AI system, keep two others as backups, and spend 90% less while getting 99.8% of the capability.

The entrepreneurs who thrive in this moment won’t be the ones chasing every new tool. They’ll be the ones who learn the underlying skills — API integration, automation architecture, troubleshooting — that let them evaluate and adopt new technology faster than competitors.

I’m currently at $41/month for my entire AI stack. What are you paying? Drop your numbers in the comments — I respond to every single one.

Upcoming: My eBay AI Automation Camp launches on the 19th. We’ll build 24/7 working agents for order processing, customer messaging, and product research — designed to get you operational in under 1 hour. Details in the community links.

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 Tested: I Cut My AI Costs from $420 to $41/Month appeared first on Digital Market Mentoring.

Introduction

Imagine having a personal assistant like JARVIS from the Iron Man movies, but in real life. This is no longer a fantasy, as demonstrated by the innovative project showcased in the video titled “I Built a JARVIS with Claude Fable 5 — 11 Screens Open With a Clap (Free Until June 22)”. In this article, we will dive into the details of this project, explore its capabilities, and discuss the implications for the future of personal digital assistants.

Key Takeaways:

• Learn how to build a JARVIS-like system with Claude Fable 5.
• Discover the capabilities of this innovative system.
• Understand the importance of taking advantage of the free offer before June 22.
• Explore the potential applications and benefits of such a system.

What is JARVIS and How Does Claude Fable 5 Help?

JARVIS, or Just A Rather Very Intelligent System, is a fictional AI assistant featured in the Iron Man movies. It is known for its advanced capabilities, such as controlling various systems, providing information, and even engaging in conversation. The video demonstrates how to create a similar system using Claude Fable 5, a new model that allows for the control of multiple screens and devices with a simple clap.

How to Build a JARVIS-like System with Claude Fable 5

1. Gather the necessary components: To start, you will need the provider’s new model, Claude Fable 5, and a set of compatible screens and devices.
2. Set up the system: Follow the instructions provided by the provider to configure the new model and connect it to your screens and devices.
3. Customize the settings: Adjust the settings to suit your preferences, such as the number of screens that open with a clap and the specific functions each screen performs.
4. Test the system: Once everything is set up, test the system to ensure it works as intended. Make any necessary adjustments to optimize its performance.

Benefits of a JARVIS-like System

A JARVIS-like system offers numerous benefits, including:

• Increased efficiency: By controlling multiple screens and devices with a simple clap, you can save time and effort when managing your digital environment.
• Enhanced convenience: The ability to access information and control devices without physical interaction makes the system highly convenient for users.
• Improved organization: With 11 screens open at once, you can easily organize and manage your tasks, projects, and information.

Frequently Asked Questions

What is the deadline for the free offer?

The free offer is available until June 22. Make sure to take advantage of this opportunity before it expires.

Is Claude Fable 5 compatible with all screens and devices?

The provider’s new model, Claude Fable 5, is designed to work with a wide range of compatible screens and devices. However, it is essential to check the compatibility list provided by the provider to ensure your devices are supported.

How can I learn more about building a JARVIS-like system?

For more information on building a JARVIS-like system with Claude Fable 5, consider joining the Skool community or booking a call with Akın Yılmaz, the operator behind the desk session.

Where can I find resources to help me get started?

To get started, you can download the free starter PDF and explore the Inner Circle for additional resources and support.

Conclusion

Building a JARVIS-like system with Claude Fable 5 offers a unique opportunity to enhance your digital environment and improve your overall productivity. By following the steps outlined in this article and taking advantage of the free offer before June 22, you can create a powerful and convenient personal assistant that will change the way you interact with your digital devices.

Watch the full transmission on YouTube: https://www.youtube.com/watch?v=w6tr-K5docw

Watch the full transmission: https://www.youtube.com/watch?v=w6tr-K5docw

The post How to Build a JARVIS-like System with Claude Fable 5: A Comprehensive Guide appeared first on Digital Market Mentoring.

Key Takeaways

• I built a 24/7 AI agency using 5 tools for roughly $40/month total, down from £400+ I used to spend
• Paperclip acts as CEO with 167 ready-made agent templates — no building from scratch
• Hermes Agent handles customer service via Telegram automatically; the GitHub repo hit nearly 100,000 stars faster than any AI agent in history
• Claude Code now works in Excel, PowerPoint, Word and Outlook — I don’t make client reports anymore
• Smartlead sends ~500 cold emails daily; 12-100 replies, ~28% convert to sales
• The critical fix: changing “AI system” to “assistant” in my messaging — people fear AI, not assistants
• It didn’t work on day 1. Three days of silence. The word change on day 4 brought the first sale

Step 1: Paperclip as Your AI CEO

I started with Paperclip — and I want you to notice the words on their site: “Zero Human Company.” That’s not hype. It’s the actual structure. Paperclip becomes your CEO, and underneath it runs a team of agents: a graphic designer, a developer, a marketer, customer service — all working 24 hours while you sleep.

The beauty is the 167 ready-made company templates. You don’t build from zero. You pick a template like a freelancer profile, or a restaurant setup where everything inside is already prepared. The agent I use is called Hermes Agent. I was the first in Turkey to cover it last month in a video about the “crazy AI computer” I got my hands on.

Paperclip manages the company through Claude. Your CMO can be OpenAI, your CTO can be Cursor, your CEO can be Claude, engineering can run on Codex — the system shows you that all of it can be automated. What used to cost me nearly $140/hour in human salaries now stays in my pocket.

Step 2: Hermes Agent for 24/7 Customer Conversations

Hermes is what sits under Paperclip as your customer representative. Here’s why this matters: the GitHub repository has nearly 100,000 stars — no AI agent in history has grown this fast. Why? Because it’s open source, free, and it actually works.

Here’s the loop: Hermes connects to Telegram. A customer writes. Hermes responds automatically. Paperclip generates the report. I check Telegram while walking outside and approve or adjust — all in seconds. I now manage my business on my walks. It’s the best thing I’ve done for myself.

The learning capability is what separates it from basic chatbots. Hermes keeps learning from interactions, doesn’t repeat the same mistakes, doesn’t ask the same questions twice. It runs completely open-source on your own computer, handling everything from video editing to whatever you can imagine. As of now, nothing has surpassed this — though something will, because something new comes every day.

Step 3: Claude Code as Your Production Engine

This is where the actual client work happens. Claude Code builds websites, automations, content — whatever you’re selling. A quick note: I covered how to run Claude Code for free with LLM models in a previous video for anyone who wants that route.

Remember I said limits doubled? Anthropic’s official announcement means I’m working twice as fast now. I deliver to clients in half the time. And on May 7 — two days ago — Claude expanded into Excel, PowerPoint, Word, and Outlook. I don’t prepare Excel reports for clients anymore. Claude makes the PowerPoint presentation and sends it.

I originally built my one-person AI company on Claude alone. That video became the most-asked-about topic on this channel, which is why I’m going deeper now. And realistically, I’m one step beyond even that video today — because the Paperclip + Hermes + Claude Code trio is the formula for transitioning from company to agency.

Step 4: Smartlead for Client Acquisition

AI means nothing without customers. This is critical. Smartlead is a cloud email sending tool that costs roughly $39/month to start. It sends nearly 500 emails daily for me. I get 12 to 100 replies, and approximately 28% of those convert to sales.

Hermes integrates with Smartlead — Hermes is the brain, Smartlead is the hand. For payments, I use Stripe. I send the link, the client clicks and pays, I deliver the service, Stripe notifies me. Some days I’ve done over £16,000 in sales. I covered the full Stripe setup in my previous video where I did £3,000 in 7 days.

Step 5: Bluehost VPS to Keep Everything Running 24/7

Your laptop won’t work — close it and the AI stops. You need a server running 24/7. I use Bluehost VPS. The starter plan at roughly $5-10/month is enough. That’s $5/month, server stays on while you sleep.

My total monthly cost breakdown:

• Smartlead: ~$39
• Bluehost VPS: ~$5
• Claude: ~$20 average
• Total: roughly $40/month

I used to spend over £400 on Claude, ChatGPT, and Cursor. Now I save nearly $300/month.

The One Thing You Must Hear Before Starting

I promised you this in step five. If you’re starting this, don’t start without hearing it: this system doesn’t work on day one. I worked three days. No response. You won’t quit. Then I changed something: word choice. I said “AI system” — I changed it to “assistant.” People are scared of AI. They’re not scared of assistants. What you’re seeing now is a system that automates everything you learned in hundreds of Shopify courses. The next day, the first sale came. Then orders kept coming continuously.

This system depends on word choice. Not the tool. Not the method. The word.

FAQ

How much does it cost to run this AI agency per month?

Approximately $40 total: Smartlead (~$39), Bluehost VPS starter plan (~$5), and Claude usage (~$20 average). This replaced over £400/month I previously spent on multiple AI tools.

What is Hermes Agent and why is it significant?

Hermes is an open-source AI agent that handles customer service via Telegram automatically. Its GitHub repository gained nearly 100,000 stars faster than any AI agent in history because it’s free, open-source, and functional. I was the first in Turkey to cover it.

How does the Paperclip + Hermes + Claude Code combination work?

Paperclip serves as CEO managing the business structure with 167 templates. Hermes operates underneath as customer-facing support. Claude Code executes the actual deliverables — websites, automations, reports, presentations. Together they form a 24/7 agency pipeline.

What was the specific change that made sales start coming in?

After three days of no response, I changed my messaging from “AI system” to “assistant.” The first sale came the next day. The tool and method were identical — only the wording changed. People fear AI but accept assistants.

Conclusion

I built this system in five steps, spent roughly $40/month, and replaced over £400 in previous tool costs. The sales hit while I was sleeping because the infrastructure runs 24/7 on a $5 server. But the technical setup is only half the story — the word choice between “AI system” and “assistant” was what actually converted. If you’re in my community, I’ve shared the exact prompts, cold email templates, SMS marketing systems, and live walkthroughs in Turkish. For anyone feeling lost in this rapid pace of change, that’s completely normal — I’ve spent nearly 15-20 years building this experience, and I’m offering free 15-minute calls to map the fastest path forward for your situation.

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 $16K/Day AI Agency in 5 Steps — What Actually Worked appeared first on Digital Market Mentoring.

Key Takeaways

• Claude Cloud Code’s new multi-agent “Agent Teams” feature (launched Feb 26 with Opus 4.6) lets one leader agent distribute work across multiple agents in parallel
• Running 7 agents in parallel on Cloud Code costs $500-$800/month vs. $200 for single-agent use
• I replaced the entire paid stack with free local models using Olama, saving approximately $7,000-$10,000 per year
• Performance gap between top local models and Claude Opus is only ~3.5 percentage points (96% of performance for 0% of the cost)
• Minimum viable setup: 16GB RAM. Recommended: 32GB+. Below 16GB, don’t bother — the system will be unusable

What Changed: The Multi-Agent Shift That Broke My Budget

A single tweet from an Anthropic engineer changed everything. The announcement was simple: Cloud Code now supports agent teams. Instead of one agent processing tasks sequentially, a leader agent distributes work across multiple team members working in parallel on different files. They message each other. The leader issues commands. Seven agents, same prompt, working simultaneously.

I saw the demo video: seven agents operating on different files at once, coordinating automatically. This was essentially unprecedented in software history — fully automated agent orchestration.

I’d been using Cloud Code heavily for my affiliate businesses — specifically for appointment scheduling and transaction processing across affiliate companies. It had generated over $73,000 for me. The other agents continuously produced content for these operations. Every company paid per completed sale.

But the dark side arrived fast. To use Cloud Code at all, you’re paying ~$200/month. Starter tier isn’t enough. You upgrade. Then upgrade again. With multi-agent mode, token usage jumps 3-4x. I was looking at $500-$800 monthly bills. Seven agents running parallel meant a swollen invoice at month-end.

Then I found the free path. And the same approach works for Hermes computer use too — you can run 7-8 processes on your desktop locally.

Step 1: Understanding What Agent Teams Actually Do

A Google Chrome engineering manager — one of the most respected names in software — blogged about this with a headline calling it “7-agent bees.” The concept is transformative.

Here’s the technical reality: Agent Teams launched February 26 with Opus 4.6. It’s still in experimental mode. Activation requires exactly one line in a JSON config file: Cloud Code Agent Teams = 1. Save, restart Cloud Code, done.

The leader agent distributes parallel work to multiple team members. Those members message each other directly. This differs fundamentally from sub-agents.

I tested it with a simple prompt: “Design a CLR tool for me.” Three team members auto-formed — one for API layer, one for database, one for test coverage. One sentence, seven agents auto-deployed. Each worked in its own context window. They conversed, completed the work.

What used to take a full day now took 25 minutes.

Step 2: The Open-Source Foundation — Olama

The secret is Olama, an open-source tool with over 90,000 stars on GitHub. It runs on Mac, Windows, and Linux. It runs AI models on your computer for free. No payments to ChatGPT, no Cloud subscriptions.

The model library is extensive: hundreds of free models including Qwen3 Coder, Nex, Alibaba’s new coding model (80 billion parameters, 3 billion active), GLM 4.7 Flash from Chinese AI company Zhipu, and GPT-OSS — OpenAI’s first open-source model.

GLM 4.7 Flash scores nearly 58.4% on Branch Pro, the highest score for open-source models. DeepSeek V4 leaked this week with 1 million token context window. All free. All running locally on your machine. Your data stays on your machine — no sharing with anyone.

Andre Karpathy, former Tesla AI director and OpenAI founder, posted on X: “As a programmer, I’ve never felt this far behind.” The post went viral. Local models are now so powerful that even industry leaders struggle to track the pace of improvement.

Step 3: The Integration Breakthrough (No More Complex Middleware)

Previously, connecting Cloud Code to local models required painful setup: Lite LLM installation, Docker containers, proxy configuration. That’s all changed.

Olama’s official documentation now shows a single command. The latest Olama version achieved direct Anthropic API compatibility. You can remove Cloud Code from your system entirely — Olama runs models that substitute for Anthropic’s.

The setup is three lines. For those who want alternatives, OpenCode, Hermes, Codex, Droid, and PID are all available through the same approach. You simply copy the model code from Olama’s library and provide it to your chosen interface.

For Cloud Code specifically: install Olama on your machine (Mac/Windows/Linux), select your platform, click the scenario, install via terminal. Then paste the model code into terminal as shown in Olama’s documentation. If you’re using OpenCode or Hermes agents instead, copy their respective codes.

Those three lines tell Cloud Code where to connect — not to Anthropic’s servers, but to Olama running on your local machine. The new Olama version even has a shortcut: olama launch cloud. This command configures three variations for you, opens Cloud Code, asks which model to use, and connects you in 30 seconds. Free, unlimited usage from that point.

Step 4: Building My 7-Agent System (Real-World Application)

Here’s my actual operational setup. Cloud Code open. Agent Teams active. Backend: Olama. Cost: $0.

I configured seven agents with this prompt structure: “I want to establish an affiliate company. Seven team members.” (I’ll share the full prompt template separately.)

Agent 1: Market research — analyze which niches and channels drive sales in your sector.
Agent 2: Content production — blogs, social media posts.
Agent 3: CEO — keyword analysis.
Agent 4: Customer service — automated response preparation.
Agent 5: Sales — cold email draft creation.
Agent 6: Analytics — performance reporting.
Agent 7: Coordination — evaluate metrics, resolve conflicts.

All seven operate on a shared task list, communicating with each other.

For non-technical users already paying for ChatGPT: after installing Olama, you can access Navy Chat (the new conversation interface) and select whichever local model you prefer. Install it, and you have a free, 24/7 operational chatbot on your computer that works offline — on planes, traveling, anywhere without internet.

I build these automation systems for large companies — 24/7 operational systems generating thousands of points in daily recurring orders. Boris Cherny, who built Cloud Code, wrote a viral post about running 3-8 parallel projects simultaneously, managing agents that write code while he only checks results. Even the creator of Cloud Code no longer writes code directly — he manages agents. The rules of software have changed.

Step 5: The Numbers and Critical Hardware Warning

Anthropic Cloud Code: $200/month base subscription. Multi-agent mode: 3-4x cost increase due to token consumption. Monthly reality: $600-$800. Annual cost: nearly £10,000, approximately $7,000-$9,000.

Cloud Code installed locally with Olama integration: $0/month. $0/year. Hardware is a one-time cost.

The performance question: benchmark scores are remarkably close. Local models hit 77.2% where Anthropic Opus 4.5 scores 80%. That’s a 3.5 point difference — 96% of the performance for free. The remaining 4% costs $10,000 annually if you want it.

New models release daily. Rather than researching which is best, I use the LLM Media Book page — all current sources aggregated there, which is where I source updates. Price, speed, capabilities all detailed by use case: coding, content creation, video generation, image generation. Targeted selection for your specific needs.

Critical hardware warning — do not skip this:

This system has one requirement: sufficient RAM. Minimum 16GB. At this threshold, small models run (Qwen 7B, Phi-3 Mini). System runs slow but functional. Recommended: 32GB RAM. This is where real power begins — faster, higher-quality model operation. With 32GB, running Qwen 27B or DeepSeek V4 local versions approaches Anthropic Opus-level performance.

At 8GB RAM: don’t install. Your computer will choke and become unusable. Upgrade RAM first, then migrate to this system. Otherwise it’s genuinely frustrating.

Alternative: use cloud VPS servers with adequate specs, install there, and operate through virtual machines.

For Mac users specifically: MacBooks use unified memory architecture. 32GB Mac versus 32GB Windows — minimal difference. But don’t test this on 8GB machines. You’ll get frustrated and abandon the system before seeing its capability.

FAQ

Is Olama actually free for commercial use?

Yes. Olama itself is open-source and free. The models it runs are free open-weight models. No subscription fees, no usage limits, no API costs. Your only expense is hardware (your computer or a VPS) and electricity.

How difficult is the technical setup for non-programmers?

The modern setup is dramatically simpler than before. Olama’s latest version requires approximately three terminal commands and a single config line. For completely non-technical users, Olama’s Navy Chat interface provides a graphical alternative requiring no command-line interaction.

Can I run this on a laptop while traveling?

Yes, and this is a major advantage over cloud-dependent systems. Once models are downloaded, Olama operates entirely offline. I’ve used it on planes and in locations without internet. The 24/7 operational capability remains regardless of connectivity.

What’s the realistic performance difference versus paid Claude?

Based on benchmark data I reviewed, top local models achieve approximately 96% of Claude Opus 4.5’s performance (77.2% vs. 80% on coding benchmarks). For most business automation tasks — content creation, data analysis, customer service workflows — this difference is operationally negligible. The gap continues narrowing as open models improve weekly.

Conclusion

I didn’t set out to eliminate my AI subscription costs. I set out to scale my affiliate operations with multi-agent automation. The cost explosion was an unintended consequence of success — more agents, more parallel processing, more tokens, more invoices.

The local alternative wasn’t just cheaper; it was complete cost elimination with near-equivalent capability. The $73,000 my systems generated wasn’t diminished by this switch — if anything, the removed cost constraint lets me run more agents more aggressively.

The hardware requirement is real. The setup requires some technical comfort. But the economic math is unambiguous: $7,000-$10,000 annually versus zero, for 96% of the performance. For entrepreneurs running AI-dependent operations, this shift from cloud to local isn’t just about saving money — it’s about removing the variable cost barrier that scales against your success.

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 Replaced $10K/Year Claude Cloud with Free Local AI — Here’s How appeared first on Digital Market Mentoring.

Introduction:

Akın Yılmaz, the London-based operator behind digitalmarketmentoring.com, has recently conducted an intriguing experiment comparing PewDiePie’s free AI with his own AI agent. The results, as he puts it, were shocking. In this article, we will delve into the details of this comparison, providing insights into the capabilities and limitations of both AI systems.

Key Takeaways:

• The comparison between PewDiePie’s free AI and Akın Yılmaz’s AI agent revealed significant differences in performance.
• Understanding the nuances of AI capabilities is crucial for leveraging them effectively in various applications.
• The test highlighted the importance of personalized AI solutions tailored to specific needs.

Comparing AI Systems:

The world of AI is rapidly evolving, with new models and applications emerging regularly. PewDiePie, a popular content creator, has recently released a free AI model, which has sparked interest among users and professionals alike. Akın Yılmaz, an expert in AI systems and ecommerce automation, decided to test this free AI against his own AI agent to see which one performs better in real-world scenarios.

Methodology:

The test involved a series of tasks designed to evaluate the AI systems’ capabilities in various areas, including language understanding, problem-solving, and adaptability. Both AI systems were given the same set of tasks to complete, and their performances were closely monitored and assessed.

Performance Analysis:

Language Understanding:

In the language understanding category, both AI systems demonstrated a strong grasp of natural language processing. However, Akın Yılmaz’s AI agent showed a slight edge in understanding context and nuances, which can be attributed to its personalized training and data input.

Problem-Solving:

When it came to problem-solving, the results were more varied. PewDiePie’s free AI performed well in structured tasks with clear guidelines, while Akın Yılmaz’s AI agent excelled in more complex, unstructured problems. This suggests that the personalization and customization of AI models can significantly impact their effectiveness in different scenarios.

Adaptability:

The adaptability test was a key differentiator between the two AI systems. Akın Yılmaz’s AI agent was able to adapt to new situations and learn from previous interactions more effectively than PewDiePie’s free AI. This adaptability is a crucial factor in the long-term effectiveness and utility of AI systems.

Frequently Asked Questions:

What was the purpose of the AI comparison?

The purpose of the AI comparison was to evaluate the performance of PewDiePie’s free AI against a personalized AI agent in various tasks, providing insights into their capabilities and limitations.

Which AI system performed better overall?

While both AI systems had their strengths, Akın Yılmaz’s AI agent outperformed PewDiePie’s free AI in terms of adaptability and problem-solving in complex scenarios.

What does this comparison tell us about AI systems?

This comparison highlights the importance of personalization and customization in AI systems, as well as the need for continuous learning and adaptability to ensure their effectiveness in various applications.

Where can I learn more about AI systems and their applications?

For more information on AI systems and their applications, consider joining the Skool community, downloading the free starter PDF, or booking a call with Akın Yılmaz for personalized mentoring. You can also apply to join the Inner Circle for more in-depth knowledge and resources.

Conclusion:

The comparison between PewDiePie’s free AI and Akın Yılmaz’s AI agent has provided valuable insights into the performance and capabilities of different AI systems. It is clear that while free AI models can offer a good starting point, personalized and customized AI solutions can deliver superior results in specific applications. As AI technology continues to advance, understanding these nuances will be crucial for leveraging AI effectively.

Watch the full transmission on YouTube: https://www.youtube.com/watch?v=CNDFGsX860E

Watch the full transmission: https://www.youtube.com/watch?v=CNDFGsX860E

The post How PewDiePie’s Free AI Compares to My AI Agent – A Comprehensive Test appeared first on Digital Market Mentoring.