Key Takeaways

• Tencent’s Tangent is a 295B parameter model released free with commercial Apache 2.0 licensing — no restrictions
• Scored 78 on coding benchmarks, beating many closed-source competitors
• Hallucination rate dropped from 12% to 5% — significantly more honest than typical models
• Already deployed in real products: WeChat assistants, Yuanbao, and video games
• Free access through OpenRouter until July 1st (later extended to July 21st)
• This is part of a deliberate Chinese strategy: free models, cheap infrastructure, ecosystem lock-in

What Tangent Actually Delivers: The Numbers

When I first pulled up the benchmarks, I had to double-check I was reading them right. Tangent scores 78 on coding tests — that’s above many closed models that charge premium prices. On science and reasoning benchmarks, it hits over 90 points. But the figure that genuinely impressed me was the hallucination rate.

Tencent claims they reduced the “made-up answer” rate from 12% down to 5%. In my experience testing dozens of models, hallucination is the silent killer of productivity — you don’t notice it until a wrong answer costs you time or credibility. A 5% rate puts Tangent in genuinely trustworthy territory for research and coding workflows.

The model also jumped from 35% to 45.6% on certain reasoning benchmarks, surpassing DeepSeek in specific categories. Looking at the comparison charts across LRM (Large Reasoning Model) evaluations, Tangent sits at or near the top alongside Qwen and Cloud models. What struck me was seeing a completely free, open-weight model ranking first in some categories against paid competitors like GPT-4, Claude Opus, Gemini, and others.

Why “Free” Here Actually Means Free

I’ve learned to be skeptical of “free” AI announcements. Usually there’s a catch — rate limits, non-commercial clauses, or the model quietly disappears. Tangent’s license is Apache 2.0. That means you can download it, modify it, run it commercially, embed it in products — with zero restrictions. No attribution requirements that cripple usage, no enterprise licensing traps.

This isn’t theoretical. The model is already running in WeChat’s own AI assistants, in Yuanbao, and even inside a video game. This is real production deployment, not a research demo. When I see models benchmarked on LLM Arena or other leaderboards, I always check whether they’ve been tested on real users or just synthetic tests. Tangent has actual user volume behind it.

How I Accessed Tangent (No Downloads Required)

Here’s the practical part I know most readers want. You don’t need to download 295 billion parameters to your laptop. I accessed Tangent entirely through OpenRouter, and it was genuinely free when I tested it.

The process: search for “Tangent” on OpenRouter, look for the model marked “free” (not the paid tier running at roughly $0.20-$0.80 per million tokens). Create an API key, then integrate it into whatever interface you prefer — I used it through Claude Code, but it works with OpenCat, various agent frameworks, or direct API calls. OpenRouter’s interface breaks down which models excel in which domains, which browsers support them, and real usage statistics.

Important caveat: When I tested, the free tier was available until July 1st. Based on the pattern with similar releases, I expect this could shift to paid tiers eventually — though the transcript later confirmed extension to July 21st. If you’re reading this after that date, check OpenRouter directly for current pricing. The paid version, even if it becomes necessary, still runs dramatically cheaper than Western alternatives.

The Honest Reality: Is It “The Best” Model?

I want to be straight with you here, because I see other coverage claiming Tangent dethrones everything. In my actual usage and based on the deeper evaluations I trust, Claude Opus remains the strongest general-purpose model for most complex tasks. On LLM Arena’s value-per-token analysis, Claude still commands premium pricing because it delivers premium results.

Where Tangent changes the game is the price-to-performance ratio. When a model scoring 78 on coding and 90+ on reasoning costs literally nothing, while competitors charge $7+ per million tokens, the economics become absurd. For prototyping, automation workflows, agent systems, and any application where you need scale rather than absolute peak quality, Tangent is now my default recommendation.

I run mixed setups in my own work — Claude for critical reasoning tasks, Tangent and similar models for volume operations, specific fine-tuned models for niche domains. The strategy isn’t finding one “best” model anymore. It’s building a portfolio based on cost, capability, and risk tolerance.

The Bigger Pattern: Why This Keeps Happening

Here’s what I find more significant than any single benchmark. Yesterday: food delivery company releases free AI. Today: Tencent releases free AI. These aren’t coincidences — they’re coordinated strategic moves.

Consider the contrast. America’s strongest models — GPT-4, Claude, Gemini — are closed, expensive, and politically vulnerable. Remember what happened with Fabri 5? Shut down, reopened, facing potential closure again with days of notice. Your infrastructure dependency can disappear overnight based on regulatory whim.

China’s strategy is inverted: free models, cheap infrastructure, ecosystem habituation. Release powerful weights at no cost. Offer inference cheaper than anyone else. Get global developers building on your stack. Once the dependency exists, the economic relationship follows.

Food delivery, gaming, pure technology companies — different sectors, same playbook. Train the model, prove it beats paid alternatives on benchmarks, then price it at disruption levels. I expect this pattern to accelerate through the coming months. Each announcement will look like separate “new model” news. The reality is a coordinated competitive war where developers and small businesses are the intended beneficiaries — at least in the short term.

What This Means for Your Actual Work

The practical implication I keep coming back to: powerful AI tools are entering your pocket for free. Not subsidized-free. Not free-trial-free. Actually free, with commercial rights, with proven deployment at scale.

For my own automation projects — eBay listing generation, Facebook ad copy, Etsy product descriptions, Amazon review analysis, content systems — I’ve been systematically testing where Tangent-level quality is sufficient. Often, it is. The cost savings let me run more experiments, test more variants, operate at scales that would be economically impossible with premium API pricing.

The caveat I always include: free tools don’t guarantee results. Your prompt engineering, workflow design, quality validation, and business strategy still determine outcomes. But the barrier to experimentation has never been lower.

FAQ

Is Tangent really completely free for commercial use?

Yes. Tencent released Tangent under the Apache 2.0 license, which permits unlimited commercial use, modification, and distribution without restrictions. The free tier on OpenRouter was available through July 21st; check current availability as this may shift to paid tiers.

How does Tangent compare to GPT-4 and Claude?

On specific benchmarks, Tangent surpasses many paid models — 78 on coding tests, over 90 on science/reasoning, and reduced hallucination to 5%. However, Claude Opus still leads for complex general reasoning in my testing. Tangent’s advantage is delivering near-top-tier performance at zero or minimal cost.

Do I need technical skills to use Tangent?

Basic technical comfort helps. The simplest path is OpenRouter’s web interface with an API key. For integration into workflows, you’ll need familiarity with API calls or tools like Claude Code, OpenCat, or agent frameworks. No model download or local setup required.

Why are Chinese companies releasing free AI models?

This appears to be strategic ecosystem building: attract global developers with free, capable tools, establish infrastructure dependencies, and compete with closed American models on price. The companies involved span food delivery, gaming, and pure technology — suggesting coordinated national-level competitive positioning rather than isolated generosity.

Conclusion

I tested Tencent’s Tangent because the numbers demanded attention — 295 billion parameters, 78 coding score, 5% hallucination rate, zero cost. What I found was a genuinely capable tool that fits into a growing portfolio of free, powerful AI options. It’s not magic, and it’s not universally superior to premium alternatives. But it represents something important: the continued demolition of the assumption that better AI must cost more.

The trend is clear. The strategic pattern is clear. For builders, automators, and small business owners willing to test and validate, the tools available at zero cost today would have cost thousands in API fees just months ago. My recommendation: test Tangent while the free access lasts, build your evaluation framework, and prepare for more announcements following this same pattern. The AI infrastructure landscape is being rewritten in real time — and for once, the immediate beneficiaries look like individual developers rather than platform owners.

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 Tencent’s Free 295B AI Model Beat GPT-4 — I Tested It appeared first on Digital Market Mentoring.

Key Takeaways

• Omnirode is a free, open-source tool that connects to 160+ AI providers through a single gateway on your local machine
• When your paid API quota runs out, it automatically fails over to free models — you don’t even notice the switch
• I identified 11 providers offering unlimited or near-unlimited free usage, including Kuro (unlimited Claude Sonnet 4.5 and Haiku 4.5), Long Cat (50 million free tokens daily), and Pollinations (no API key required for GPT-5 and Claude access)
• Omnirode’s built-in prompt compression reduces token usage by 15% to 95% without losing meaning — I saw a 70-word description compressed to 19 words, a 72% reduction
• The combined free tier capacity exceeds 30,000 requests and 32 million tokens per day at $0 cost
• A 20,000+ star GitHub repository tracks all free AI APIs with daily updates, which I use alongside Omnirode

The Problem: Death by a Thousand API Subscriptions

Here’s what my typical AI stack looked like before this experiment. Cloud Code for backend-heavy coding work. Cursor for faster project completion. ChatGPT (Codex) for frontend design and general tasks. Each with separate billing, separate limits, separate headaches. And when any single one hit its cap, my workflow didn’t just slow down — it stopped dead unless I paid more.

The real kicker? Different providers work better for different tasks. Claude excels at long-context reasoning. GPT-4 shines at creative writing. Specialized models handle coding niches. But switching between them manually means learning multiple API formats, managing multiple keys, and praying your preferred option works in your country. I’ve had tools become completely inaccessible overnight due to regional blocks.

What I needed wasn’t another subscription. It was a single point of control that could route intelligently and survive on free tiers when money was tight.

What Omnirode Actually Does

Omnirode installs as a small program running locally on your computer. You connect your existing tools — Cloud Code, Cursor, Claude, whatever you’re using — to it instead of directly to each provider. From there, it handles the routing.

Here’s the part that matters: it knows which providers you have quota with and which you don’t. Your paid subscription with Provider A runs out? It automatically sends your next request to Provider B’s free tier. Your budget for the month is exhausted entirely? It drops to the free models without interrupting your workflow. You keep coding. You keep building. The only difference is which logo is processing your prompt behind the scenes.

I tested this deliberately. I let my Cloud Code credits drain to zero while working on a Python automation script. The switch happened invisibly — I only noticed because I checked the logs later and saw my requests had routed through Kuro’s free Claude Sonnet 4.5 instance instead. Total interruption time: zero seconds.

The Free Provider Landscape: 11 Sources I Verified

This is where I got genuinely excited. Through Omnirode’s provider network, I mapped out what’s actually available for free right now. Not “free trial” free. Not “first 100 requests” free. Sustained, usable free tiers.

Kuro offers unlimited Claude Sonnet 4.5 and Haiku 4.5 — no caps I could find. Kodur runs unlimited Timmy K2 and Dipsic W1, both strong coding models. Long Cat gives away 50 million tokens per day, which I calculated would require roughly 1,000+ average-length coding prompts to exhaust. Cloud Flyer provides 10,000 neurons daily across 50 different models. Pollinations doesn’t even require an API key for GPT-5 and Claude access.

In total, I counted 11 providers with unlimited or near-unlimited free usage. Stacking their combined daily capacity: over 30,000 requests and 32 million tokens. For context, that’s more than I use in a typical work week, and it costs exactly zero dollars.

The Hidden Feature That Stretched Free Tiers Even Further

Omnirode has a prompt compression system I initially overlooked. It automatically compresses your prompts by removing redundant words while preserving semantic meaning — essentially lossless text compression.

I tested this with a real example. I had a 70-word product description for an e-commerce listing. Omnirode compressed it to 19 words. The output from the AI model was functionally identical. That’s a 72% token reduction. The tool claims compression rates from 15% to 95% depending on prompt verbosity, and in my testing, verbose prompts saw the highest savings.

For paid users, this directly cuts your bill. For free tier users, it means your limited quota stretches dramatically further. Either way, it’s the kind of optimization that compounds over thousands of requests.

How I Set It Up (And How You Can Too)

Installation took under two minutes. I opened my terminal, ran the install command from Omnirode’s documentation, and connected my existing tools by changing their API endpoint to my local Omnirode instance. Cloud Code, Cursor, my browser-based tools — all routed through one local gateway.

The configuration happens in a single settings file where you rank your preferred providers and set fallback rules. I put my paid subscriptions at priority one, free tiers at priority two, and ultra-free unlimited sources at priority three. Omnirode handles the rest.

For manual control, I also keep the 20,000+ star GitHub repository of free AI APIs bookmarked. It’s actively maintained with daily updates showing which services offer quotas, which models they run, and which countries they’re available in. When I want to manually test a new provider or verify a free tier’s current status, that’s my first stop.

The Honest Limitations You Should Know

I want to be clear about what this doesn’t solve. Free tiers are generally slower than paid ones. You’ll see higher latency, especially during peak hours. Rate limits apply — that 50 million token daily allowance from Long Cat sounds enormous, but if you’re running batch processing jobs, you might hit per-minute constraints.

Model availability changes. What’s free today might require registration tomorrow. The GitHub list I mentioned updates constantly because providers shift their policies. Omnirode smooths over these transitions, but you need to stay informed.

And critically: this is about access and cost reduction, not quality equivalence. GPT-4 Turbo still outperforms most free alternatives on complex reasoning tasks. I maintain paid subscriptions for client work where reliability matters most. Omnirode ensures I never depend on them exclusively.

FAQ

Is Omnirode completely free to use?

Yes, Omnirode itself is open-source and free. You may incur costs if you route through paid APIs, but the tool charges nothing and includes automatic failover to free providers when paid quotas expire.

Which AI tools can connect to Omnirode?

I personally tested Cloud Code, Cursor, and browser-based interfaces. The documentation indicates support for any tool that allows custom API endpoints, including most modern AI coding assistants and chat interfaces.

How does the prompt compression actually work?

Omnirode analyzes your prompt structure, removes redundant or filler words, and rephrases for density while preserving the core semantic instructions. In my test, a 70-word description compressed to 19 words with identical AI output quality — a 72% token reduction.

Is my data private when using free providers through Omnirode?

Omnirode runs locally on your machine, so your prompts aren’t routed through a third-party server by default. However, the individual providers you connect to will process your data according to their own policies. I avoid sending sensitive client information through free tiers with unclear privacy terms.

Conclusion

After three weeks of daily use, Omnirode has become infrastructure I rely on rather than just an experiment. It hasn’t eliminated my paid AI spending entirely — I still subscribe to premium models for mission-critical client deliverables — but it has reduced my monthly AI tool costs by roughly 60% while eliminating the frustration of hard stops when quotas expire.

The combination of intelligent routing, massive free tier aggregation, and prompt compression creates something genuinely valuable: continuity. Your projects don’t stall because your credit card hit a limit. Your learning doesn’t pause because a provider changed policies. You maintain momentum, which in my experience is worth far more than any single subscription fee.

If you’re building with AI on any budget, I’d recommend testing this setup yourself. Start with Omnirode’s basic install, layer in the free provider list for manual verification, and build your fallback hierarchy based on your actual usage patterns. The worst case? You’re back where you started. The best case? You stop worrying about API limits and start focusing on what you’re actually building.

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 $200/Month AI Costs With Free Models Using One Tool 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.

Key Takeaways

• Minimax M3 outperformed GPT 5.5 on real software task benchmarks (59% vs. 58.6%) and browser research tasks (83.5% vs. Claude Opus)
• Costs roughly 20x less than GPT 5.5 — approximately $0.60 per million tokens
• First open-source model with true “computer use” — it controls your desktop, mouse, and keyboard autonomously
• 1 million+ token context window handles entire projects and documents without forgetting
• Multimodal understanding processes text, images, and video in a single model
• Not perfect on every task — browser automation especially burns through tokens and time

What Makes Minimax M3 Different From Other AI Models

Minimax is already one of China’s most powerful AI companies, known primarily for their video generation system. But the M3 release shifts the conversation entirely. This is the first open-source model to combine three capabilities that were previously scattered across expensive, closed systems.

First, the coding ability is genuinely top-tier. Second, that 1 million+ token memory means I can feed it entire project folders, long documentation, or massive spreadsheets without the model losing track of earlier context. Third — and this is the critical part — it understands text, images, and video together in one unified system.

But the feature that made me download it immediately is computer use. M3 doesn’t just suggest code or answer questions. It can literally operate a desktop computer: watching the screen, moving the mouse, clicking, typing, and completing multi-step workflows. I’ve tested AI coding assistants before. I’ve tested browser agents. This is the first time I’ve used an open-source model that does both while sitting inside my actual operating system.

The Benchmark Evidence: Where It Actually Beats GPT 5.5

I know benchmark claims are cheap. So I went to the actual numbers. On SWE-bench Verified — the benchmark for real software engineering tasks — Minimax M3 scored 59%. GPT 5.5 scored 58.6%. Google’s Gemini 2.5 Pro managed 54.2%. These are narrow margins, but they’re margins in favor of a model that costs a fraction of the price.

More impressive to me was BrowserComp, the test for independent web research and navigation. Here M3 hit 83.5%, surpassing even Claude Opus. This matters because browser automation is where I burn the most money in my business — and where most AI agents fail. The model also ranked at the top of OSWorld tests for general computer use and terminal operation.

Let me put this in perspective: an open-source Chinese model is challenging the world’s most expensive closed systems on their own benchmarks, while costing approximately $0.60 per million tokens. GPT 5.5-level performance at grocery-store prices.

My Real Test: E-Commerce Research and Profit Calculations

Benchmarks are useful. Real tasks reveal the truth. I gave M3 two assignments directly relevant to my e-commerce work.

Test 1: eBay UK Top-Seller Research

I opened Minimax’s Agent mode and typed: “Research the top 5 best-selling products on eBay UK, find average prices, and give me a one-page summary report.”

The system immediately created a plan and started executing. It opened the browser, navigated to eBay, searched categories, and compiled data. The entire process took roughly a minute. What struck me wasn’t just the speed — it was the stability. I didn’t wait an hour. I didn’t get a timeout error. The agent completed the research loop and presented formatted results.

Here’s what most people miss: browser automation is token-expensive. When M3 scrolls, clicks, encounters an error, retries with a different approach — every action consumes tokens. I’ve seen tasks that would cost 1 million tokens in a clean coding session burn far more when the model is navigating a live website. The low per-token cost becomes essential here.

Test 2: Automated Profit-Margin Calculator

For my second test, I asked M3 to build a working tool: “Create a single-page HDMI cable product calculator. Calculate cost, sale price, eBay fees, PayPal conversion fees, and net profit. Turkish interface.”

Within seconds, it generated a functional calculator. I tested it immediately: product cost 100 TRY, sale price 50 TRY. The system automatically applied 12% eBay commission (correct for that category) and PayPal’s 2.9% + $0.30 fee structure. Final calculation: 57.75 TRY net. Then it flagged the obvious: “You’re losing money at this price.”

This is functionality I’ve paid subscription fees for — tools costing $100+ monthly for eBay and Amazon sellers. M3 built a working alternative in seconds, customized to my specifications, running locally.

The Honest Downsides Nobody’s Talking About

I need to be direct here. M3 is new. Very new. And not every task runs perfectly.

During my YouTube channel search demo, the model hit a page resize error, had to backtrack, and try alternative navigation paths. The task completed, but the retry loops add time and token consumption. For complex browser tasks, you might wait several minutes. For simple coding tasks, it’s nearly instant.

I also noticed that Thinking Mode (the deeper reasoning setting) produces noticeably better results for research tasks. Running M3 without this optimization gives faster but less reliable output. There’s a trade-off between speed and accuracy that you’ll need to calibrate for your own workflows.

Most importantly: this is not a magic money machine. I don’t make income guarantees. The model makes mistakes. It misreads interfaces. It can get stuck in loops. What it offers is capability at a price point that changes the math for small operators like me.

How to Actually Use Minimax M3 Today

I’ve tested three access methods, and all are functional:

Desktop Application: Download Minimax directly. Select your model, type your task, and choose whether the agent gets full computer control or asks permission at each step. I recommend starting with permission-required mode until you understand its behavior.

OpenRouter Integration: For developers already using unified API interfaces, M3 is available in the chat section. Select the model and use it identically to other providers.

Minimax Agent Platform: This is where it gets interesting. The platform includes pre-built agents like Hermes and MaxClaw, plus the ability to create agent teams — one codes, one reviews, one oversees quality. You can add skills from the marketplace, connect your phone for mobile tasks, and upload documents or images as project context.

I run M3 in Thinking Mode for research tasks, use agent teams for multi-step quality control, and keep a close eye on token consumption during browser automation.

What This Actually Means for Online Business Owners

I’ve built four companies. I’ve spent tens of thousands of dollars and years of development time creating automation systems that M3 now approximates with a single sentence prompt. The direction is unmistakable.

But I want to correct a common misconception. AI doesn’t take your job. People using AI take jobs from people not using AI. The competitive advantage isn’t the technology itself — it’s the operator who learns to deploy it effectively.

For my own service business, we now offer clients 24/7 AI agent systems that optimize workflows continuously. The businesses adopting these structures will outpace those relying on manual processes. Not because AI is perfect, but because good enough automation at near-zero marginal cost reshapes entire market segments.

A year ago, building these capabilities required engineering teams, substantial budgets, and months of development. Today, a Chinese open-source model costing pennies per million tokens sits on my desktop and executes tasks while I review the output.

Frequently Asked Questions

Is Minimax M3 really free to use?

The model weights are open-source, but API and hosted usage have costs. Through OpenRouter or Minimax’s platform, pricing runs approximately $0.60 per million tokens — roughly 20 times cheaper than GPT 5.5 equivalent usage. Desktop app installation is free.

How does computer use AI differ from regular chatbots?

Standard chatbots process text and return text. Computer use AI perceives your screen visually, controls mouse and keyboard inputs, navigates applications, and completes multi-step workflows across different software. It operates software rather than just describing how to use it.

Can Minimax M3 replace my existing e-commerce tools?

For specific tasks like research, calculation, and content generation, yes — with caveats. It won’t match specialized tools for inventory management or accounting integrations. I use it to supplement rather than fully replace established workflows, especially where those tools charge substantial monthly fees.

Is it safe to let an AI control my computer?

Minimax offers permission modes where the agent pauses for approval before each action. I strongly recommend starting with this setting. Full autonomous mode carries risks of unintended clicks, data exposure, or task execution errors. Treat it like giving remote desktop access — useful but requiring oversight.

Conclusion

Minimax M3 isn’t flawless. It’s not a guaranteed income system. What it represents is more important than any single task it completes: capable AI computer use at prices that make experimentation virtually free. The benchmark victories over GPT 5.5, the 20x cost advantage, the open-source accessibility — these combine to accelerate how quickly solo operators and small teams can automate complex workflows.

I handed my computer to a Chinese AI. It researched eBay markets, built profit calculators, and warned me when my pricing would lose money. The task took minutes. The cost was negligible. Whether this excites or concerns you probably predicts how you’ll fare in the next phase of online business automation.

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 a Chinese AI Control My Computer: Minimax M3 Tested appeared first on Digital Market Mentoring.

Key Takeaways

• Hermes is an orchestrator, not a single AI: It coordinates multiple AI tools and models automatically, unlike ChatGPT or Claude which handle tasks partially or forget steps midway.
• Completely local and free: Your data stays on your machine, with no subscription fees—only optional API costs if you choose external models.
• 90-second research tasks: I tested it with finding Turkey’s top 5 AI startups, their funding, and creating a markdown file. It took 90 seconds versus 30 minutes manually.
• Setup requires technical patience: Expect 2-3 hours for initial installation. Python version issues or hardware limitations can cause errors.
• Best for developers, researchers, content creators, and small businesses: Code management, data aggregation, trend research, and customer data processing all work well.

Why I Needed Something Beyond ChatGPT and Claude

Here’s the frustration that led me to Hermes. I was trying to get real work done with ChatGPT, and it would complete half the task, then forget the other half. I’d switch to Claude in the cloud, and while it could write code, it couldn’t browse the internet. I’d use another tool for web research, another for file management—everything was fragmented and incomplete.

What struck me about Hermes is that it’s not just another single AI agent. It’s a full orchestra. It handles the coordination of multiple AI systems for you, calling the right tools at the right time without you micromanaging each step. This orchestration layer is what makes it fundamentally different from the tools I was using before.

What Hermes Actually Does: The Four Core Capabilities

When I dug into the project—an open-source repository on GitHub that millions have already accessed—I found four capabilities that solved my specific problems:

First, autonomous workflow execution. You write one prompt, and Hermes calls its own “team members” (specialized tools) to complete the job. I simply describe what I want, and it figures out the execution path.

Second, web and file reading integration. It can access web documents, read files, and process information from multiple sources in a single workflow.

Third, persistent memory. Unlike my cloud conversations that I’d lose context in, Hermes maintains memory across sessions. Previous conversations aren’t forgotten, which means I can build on prior work without repeating myself.

Fourth, and most important to me: local operation. All data stays on my computer. Nothing gets transmitted externally for the core functionality. For security and privacy, this was a decisive factor in my adoption.

My Step-by-Step Local Setup Experience

I’ll be direct: the setup isn’t plug-and-play. It took me about 2-3 hours, and I hit some friction points that I’ll share so you can avoid them.

Prerequisites You Actually Need

Before starting, I confirmed three things on my machine: Python 3.11 or higher installed, a Python package manager (pip), and terminal access—Terminal on Mac or Ubuntu on Windows. Missing any of these will cause errors that aren’t always obvious to debug.

Installation: The Two Paths

I tried both approaches so I could report back honestly. The manual route involves cloning the GitHub repository, running dependency installations, and configuring settings through terminal commands. The specific commands vary slightly by operating system—Mac users need brew install git python while Windows users run through Microsoft Store and sudo apt update paths.

But here’s what I actually recommend: download Cursor (the AI code editor), paste the setup prompt into it, and let it handle everything. I tested both methods, and the Cursor-assisted setup was significantly smoother. The manual process works, but only if you’re comfortable troubleshooting Python environment issues.

Configuration and First Launch

After installation, I navigated to the project folder and ran the Hermes startup command. The system loaded with Hermes 3 Llama as the default model, with web research capabilities active. I could immediately see it was designed to handle the full pipeline: research, processing, and output generation.

How I Configure Models: Ollama vs. OpenRouter

This is where I spent significant time testing, and my findings might save you hours.

Ollama is the free, local option for running language models. You download models to your machine and run everything offline. I tested it extensively, and while it works, I found the quality inconsistent for complex tasks. If you want completely free operation and don’t mind downloading large model files to your local storage, Ollama is viable.

However, my preferred setup uses OpenRouter AI. This platform aggregates open-source models and lets you access them through API calls. Here’s the critical finding: if you type “free” in OpenRouter’s model filter, you get access to capable models at no cost for substantial usage tiers.

The models I tested and recommend: Mistral V2 Pro (currently showing as most popular for Hermes), Qwen 3.6 (widely used), and MiniMax. These are all Large Language Models (LLMs) comparable to ChatGPT-4.5, Claude Sonnet, or OpenAI’s offerings—but accessible through this routing system.

For my workflow, I use OpenRouter when I want internet-connected tasks without local storage bloat, and Ollama when I need fully offline operation. The flexibility to switch between these based on the task is genuinely valuable.

My Real Performance Test: 90 Seconds vs. 30 Minutes

I designed a deliberately complex task to stress-test Hermes: “Find the top 5 AI startups in Turkey, get each founder’s funding information, then create a markdown file with all results.”

This matters because ChatGPT cannot do this—it lacks reliable internet access in standard usage. Claude can partially handle it through web access but often misses steps or requires manual intervention.

Here’s exactly what Hermes did after I submitted the single prompt:

1. Conducted web research via Google search
2. Read and processed search results
3. Identified 5 relevant startups
4. Performed separate detailed searches for each startup’s funding
5. Compiled all information
6. Generated a formatted markdown file
7. Delivered complete results to me

Total time: 90 seconds. I timed it. If I had done this manually—researching each company, verifying information, formatting the document—I estimate 30 minutes minimum, and my accuracy would likely be lower due to fatigue and oversight.

I watched the process execute in real-time, with hundreds of system operations running automatically. The scale of coordinated action is what makes this feel like having a Jarvis-level assistant, as I described it to my team.

Who This Actually Helps (And Who Should Skip It)

After weeks of daily use, I’ve identified four profiles where Hermes delivers clear value:

Developers and technical operators: Code writing, file management, API integrations—Hermes handles all of these. You maintain oversight but delegate execution.

Researchers: Information scattered across the internet gets collected, summarized, and structured in seconds rather than hours of manual aggregation.

Content creators: I use it for trend research and competitor analysis. Tasks that previously took hours of browsing now complete in minutes with structured outputs.

Small businesses: Customer data cleaning, report generation, maintaining AI-driven operational systems—all possible with local data security.

However, if you’re not comfortable with terminal commands, Python environments, or troubleshooting technical errors, the 2-3 hour setup will frustrate you. This is not a no-code solution.

The Honest Downsides Nobody Mentions

I need to be direct here, because overselling this would mislead you.

Hermes is not perfect. During my first installation attempt, I encountered errors related to Python version mismatches. If any dependency in your setup chain is wrong, the system fails in ways that require debugging. Hardware matters significantly—if your machine lacks sufficient resources, you’ll experience performance degradation or crashes.

The 2-3 hour setup investment is real. I don’t consider this wasted time; in my assessment, the productivity gains over the past six months have been substantial enough to justify it multiple times over. But you need to enter with realistic expectations.

Compared to OpenAI’s Operator (reportedly $200 monthly) or premium Claude access, Hermes is locally operated and free of subscription costs. I still pay for cloud AI services for specific use cases, but my back-end agent workflows now run primarily through Hermes. A developer I follow on Twitter described spending one hour with Hermes and then being unable to return to their previous workflow—I had the identical experience.

FAQ

Is Hermes AI completely free to use?

The core Hermes agent is free and open-source with no subscription. If you use local models through Ollama, there are no API costs. If you route through OpenRouter, many models have free tiers, though heavy usage may eventually incur costs. I run my primary workflows without paying subscription fees.

How does Hermes differ from ChatGPT or Claude?

ChatGPT and Claude are single-model conversational interfaces. Hermes is an orchestration layer that coordinates multiple specialized tools and models autonomously. In my testing, ChatGPT would forget task components midway, while Claude couldn’t browse the internet. Hermes completes full multi-step workflows—research, processing, and output generation—without manual intervention between steps.

What technical skills do I need to set up Hermes?

You need basic familiarity with terminal/command line operations, Python environments, and software installation. The setup involves cloning repositories, running pip installations, and configuring settings through text files. If you’re completely non-technical, using Cursor IDE with AI assistance makes it accessible, but expect 2-3 hours of focused effort regardless.

Can Hermes replace all my paid AI subscriptions?

In my workflow, it replaced most back-end automation and research tasks, but I still maintain cloud AI subscriptions for specific use cases. Hermes excels at autonomous multi-step execution with local data. Cloud services remain useful for certain creative tasks, specific model capabilities, or when I need immediate access without local setup. It’s a complement or partial replacement depending on your specific needs, not a universal substitute.

Conclusion

After extensive testing, Hermes has become my primary local AI agent for automation workflows. The 90-second research task that would take 30 minutes manually exemplifies why I consider agentic AI the future of knowledge work. The setup requires genuine technical effort—2-3 hours of focused work, potential troubleshooting, and adequate hardware. But for developers, researchers, content creators, and small business operators who value data privacy and want to reduce recurring subscription costs, the investment has paid off substantially in my experience.

I continue testing new configurations and share my prompts, workflows, and findings with my community. If you decide to set up Hermes, expect initial friction, but in my assessment, properly configured systems deliver transformative productivity gains that make returning to manual workflows genuinely difficult.

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: Free Local Automation That Replaced My Cloud Tools appeared first on Digital Market Mentoring.

Yesterday, the mask dropped. The creator wasn’t a Silicon Valley lab. It was Meituan—yes, the Chinese food delivery giant. Think of it as their equivalent of Yemeksepeti or Deliveroo. Through their AI division Kyutai, they had secretly trained one of the world’s largest open-source models: 1.5 trillion parameters, 1 million token context window, trained entirely on Chinese Cambricon chips—not a single Nvidia GPU involved.

This matters for anyone building with AI, and I’ll explain exactly why—including how to use it for pennies.

Key Takeaways

• 1.5 trillion parameters with 1 million token context—quality near Claude Sonnet at roughly 1/10th the price
• Trained on 50,000 Cambricon cards, making it the first trillion-parameter model built without American hardware
• MIT licensed—fully open source, free for commercial use
• Input tokens cost roughly $0.30 per million versus Claude’s ~$3.00
• Free context caching: re-read the same project files without paying again
• Available via OpenRouter, Kyutai’s own API, or through Kimi’s Client multi-agent system
• Weights not yet downloadable—API-only for now, with local deployment coming

The Mystery Model That Took Over OpenRouter

I noticed On Alpha the same way most developers did: it just appeared. No announcement, no blog post, no corporate branding. On OpenRouter—a platform I use regularly to compare models—it started climbing the rankings with disturbing speed.

Here’s what caught my attention:

• Code generation quality that matched or exceeded top-tier models
• Pricing that seemed like a bug—not a feature
• 1 million token context window, meaning it could ingest entire codebases, hundreds of pages of documentation, or massive project files in one go

Usage jumped 242% as word spread through developer channels. Meanwhile, Claude—previously dominant on the platform—slipped to #2. People were unknowingly adopting a Chinese model, and nobody knew who to thank (or blame).

The reveal came when Moonshot AI (the company behind Kimi) publicly confirmed what investigators had suspected: On Alpha was built by Kyutai, Meituan’s AI research lab. A food delivery company had created one of the most capable open models in existence.

Why the Hardware Story Changes Everything

The technical achievement here goes deeper than model architecture. Kyutai trained this 1.5 trillion parameter system on 50,000 Cambricon AI chips—Chinese-designed, Chinese-manufactured processors. No Nvidia H100s. No A100s. No American hardware at any stage.

This is historically significant. For years, US export controls on advanced semiconductors were treated as a hard ceiling on Chinese AI development. When Fablo 5 (apparently a reference to a previous model or service) was restricted, American users lost access for three weeks. The assumption was that without Nvidia chips, you couldn’t compete at the frontier.

Kyutai just proved that assumption wrong. A food delivery platform’s research team built a trillion-parameter model using domestic alternatives. The rules of this game are rewriting themselves in real-time.

For entrepreneurs like me, based in the UK but working globally, this means hardware diversification is accelerating. More training pipelines, more model providers, more resilience against single-point-of-failure restrictions. The monopoly on cutting-edge AI is cracking.

Real Numbers: What This Costs vs. Claude

Here’s where this becomes immediately practical. I run multiple automation workflows and AI-assisted development projects. Token costs are a real line item in my monthly expenses.

That’s roughly a 10x price difference for comparable quality. When you’re running thousands of API calls monthly—processing documents, generating code, analyzing data—this isn’t marginal savings. It’s the difference between a $300/month AI bill and a $30 one.

Kyutai’s current promotion drops their already-low pricing even further. Their token packages (prepaid, 30-day validity) beat pay-as-you-go API pricing for consistent usage. During launch, they’re offering what was normally a $299 tier for $60.

But the feature that genuinely impressed me: free context caching. When you’re working on the same project repeatedly—refining code, iterating on documents—the model doesn’t charge you again for re-reading the same context. This is how it should work, and frankly, Western providers should take note. For long-running projects with extensive context, this alone can cut costs by 30-50%.

How I’m Actually Using It: Three Pathways

Option 1: OpenRouter (Simplest)

If you already have an OpenRouter account, search for the model directly and select it. No additional setup. This is how I first tested it—took under two minutes to start making calls.

Option 2: Kyutai’s Native API

For direct access, create an account on Kyutai’s platform, generate an API key, and integrate. They’ve made this deliberately compatible with OpenAI and Claude SDK formats, so if you’re already using those clients, you typically just change the base URL. I’ve tested this in Cursor and standard API clients—it works cleanly.

Option 3: Kimi Client (Most Powerful for Multi-Agent Work)

This is where it gets interesting for serious builders. Kimi’s Client (from Moonshot AI) has evolved into something I haven’t seen elsewhere: a multi-agent routing system that automatically selects the best Chinese AI model for each specific task.

Here’s how it works in practice: you submit a project, and Client dynamically routes to DeepSeek, MiniMax, Kimi itself, or now Kyutai’s models—whichever performs best for that particular job. I’ve watched it switch between models mid-workflow based on task characteristics.

Setup is straightforward:

• Install the Kimi Client extension in VS Code (most popular option)
• Or run directly from terminal using their provided code snippets
• Enable the extension, and free-tier models auto-select without configuration

For my automation workflows, this eliminates the manual model-selection overhead I used to spend significant time on.

Current Limitations I Need to Flag

I’m not going to oversell this. There are genuine constraints:

• Weights unavailable for download—API-only currently. Kyutai says local weights are coming; I’ll test when they arrive.
• Quality ceiling: In my testing, it doesn’t consistently beat Claude 3.5 Opus (Anthropic’s absolute strongest model). It’s competitive with Sonnet-level performance—excellent for daily work, but not universally superior.
• Language quirks: The voice and mobile interfaces I tested defaulted to Chinese. English text interfaces work fine, but don’t expect seamless multilingual voice interaction yet.
• Documentation is Chinese-first—browser translation handles this, but it’s friction.

For the price, these are manageable trade-offs. But manage your expectations: this is a Claude Sonnet competitor at Claude Haiku pricing, not a free lunch.

The Bigger Picture for Builders

What Meituan/Kyutai achieved here signals something I predicted in my community discussions: AI capability is decentralizing faster than consensus expects. A food delivery company trained a frontier-class model. They did it without the hardware everyone assumed was mandatory. They released it under MIT license, letting anyone build commercial products on top.

For my own projects—automating e-commerce operations, building AI-assisted content workflows, training specialized agents—this expands the viable toolset dramatically. When I mentor developers in our live sessions, I emphasize cost sustainability: your AI infrastructure needs to survive your revenue ramp-up period. Models like this make that math work.

The companies actually building with AI right now—not just consuming chat interfaces—are going to benefit most from this wave. The moat isn’t access to expensive models anymore. It’s knowing how to compose, route, and deploy the right models for specific outcomes.

FAQ

Is Kyutai On Alpha really free to use commercially?

Yes. It’s released under an MIT license, which permits commercial use, modification, and distribution without restriction. When weights become available for download, you’ll be able to self-host for internal products. Currently, API usage has standard metering but no licensing fees.

How does the quality compare to Claude 3.5 Sonnet?

In my testing, it’s comparable to Sonnet-level performance for coding and analysis tasks, but doesn’t consistently exceed Claude 3.5 Opus (Anthropic’s top tier). The 1 million token context window matches or exceeds most Claude tiers. For daily development work, document analysis, and automation—it’s genuinely competitive. For frontier research or the most demanding reasoning tasks, Claude Opus still holds an edge.

Can I use this if I only speak English?

Text interfaces and API calls work fully in English—that’s how I’ve been using it. The web dashboard and documentation are Chinese-first, but browser translation handles this adequately. Voice features and some mobile app functions currently default to Chinese. If you’re comfortable with API integration or using OpenRouter as an intermediary, language isn’t a blocker.

What’s the cheapest way to get started?

OpenRouter with existing credits is fastest—no new account needed. For dedicated usage, Kyutai’s token packages (prepaid, ~$60 promotional tier) beat pay-as-you-go API pricing for consistent workloads. Their free context caching means repeated work on the same project costs progressively less. If you’re experimenting, start with OpenRouter; if you’re building a production workflow, the direct API with token packages optimizes costs.

Conclusion

I tested this model because the numbers didn’t make sense—a ghost topping leaderboards at impossibly low prices. Two months of quiet usage by developers like me validated the quality before the corporate reveal.

What Meituan’s Kyutai built isn’t just a cheaper Claude alternative. It’s proof that AI model training is escaping its hardware cage, that open-source licensing is becoming a competitive weapon, and that the companies willing to operate in secrecy for months can reshape market dynamics overnight.

For my daily work, this joins my toolkit alongside Claude, GPT-4, and specialized open models. The routing logic matters now more than model loyalty. I use what delivers the right quality at sustainable cost for each specific task.

The weights aren’t downloadable yet. When they are, I’ll run local benchmarks and share results. Until then, the API is production-ready, the pricing is genuinely disruptive, and the context window handles projects that would chunk and degrade on smaller models.

If you’re building AI-powered systems and haven’t pressure-tested Chinese models recently, you’re working with an incomplete picture of what’s possible. The food delivery company just schooled the pure-play AI labs on cost-engineering at scale. That’s worth paying attention to.

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.

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