INSANE AI News: NEW GPT 5 + Chinese AI Super Agents

This is the most unseen AI news ever. New GPT5 plus Chinese AI super agents. Today I'm going to show you the new GPT5 that just dropped and the Chinese AI super agents that are making Silicon Valley panic. Open AAI just launched GPT5 and it's free for everyone. China's new AI agents can do 100 tasks at once while you sleep. This is the biggest AI news of the year and nobody's talking about it. Hey, if we haven't met already, I'm the digital avatar of Julian Goldie, CEO of SEO agency Goldie Agency. Whilst he's helping clients get more leads and customers, I'm here to help you get the latest AI updates. Listen up, something massive just happened in AI. Open AAI dropped GPT5. But that's not even the craziest part. Chinese companies are releasing AI agents that are absolutely destroying what we thought was possible. And I'm about to show you exactly why this changes everything for your business. Let me start with GPT5 because this thing is insane. I've been testing it and I can tell you right now, this isn't just another update. Sam Alman teased us for weeks. He kept saying something big was coming. Then GPT5 drops and it's everything we hoped for and more. But here's what most people don't get yet. This isn't just GPT4 with a new number. This is completely different. And I'm going to show you exactly why this changes everything. First, what GPT5 can actually do. Remember how GPT4 was pretty good at coding? Well, GPT5 just scored 74. 9% on swbench verified. That's a test where AI has to solve real coding problems from GitHub. To put that in perspective, that beats every other AI model out there. We're talking about AI that can now code better than most human programmers. But wait, GPT5 scored 94. 6% on advanced math problems. These are college level math questions, and this thing solves them like it's basic edition. GPT5 isn't just one model anymore. It's actually a smart system that decides which version of itself to use based on what you're asking. Think about it like this. You have a team of experts. One is really fast and good at simple stuff. Another one is slower but incredibly smart for hard problems. GPT5 automatically picks which expert to use for your specific question. You don't have to think about it. You just ask and it figures out the best way to help you. And for the first time ever, free users get access to reasoning AI. That means even if you don't pay for chat GBT, you can now use AI that actually thinks through problems step by step. This is huge because reasoning AI used to cost hundreds of dollars per month. GPT5 is 45% less likely to make factual errors compared to GPT4. When it's using its thinking mode is 80% less likely to hallucinate or make stuff up. You know what this means? You can finally trust AI to handle important business tasks without constantly fact-checking everything. Customer service, content creation, data analysis, all of this becomes way more reliable. This thing can do what Open AI calls vibe coding. You describe what you want and GPT5 builds it. No technical knowledge needed. No coding skills required. Just describe your idea. But here's what's really crazy. GPT5 is better at tool calling. That means it can use multiple different tools and apps to complete complex tasks. It can search the web, create documents, analyze data, and coordinate between different systems all by itself. And the speed improvement is insane. Sam Alman said, "Going back to GPT4 feels miserable now. I can confirm this is true. GPT5 responds so much faster that GPT4 feels broken in comparison. GPT5 can handle complex workflows. It can manage entire projects. It can create business strategies, analyze competitors, write sales copy, and execute marketing campaign. Now, let me tell you about GPT5 Pro. This is the premium version that thinks even longer and harder about complex problems. It scored 88. 4% on GPQA, which are extremely difficult science questions. We're talking PhD level stuff. If you're in a technical field or you need AI for complex analysis, GBT5 Pro is going to be worth every penny. But even the regular GPT5 is light years ahead of anything we've had before. The instruction following is perfect, the reasoning is incredible, and the safety features mean you can trust it with sensitive business information. Speaking of safety, GPT5 introduces something called safe completions. Instead of just refusing to answer certain questions, it gives you helpful high-level information while staying within safety guidelines. This is huge for businesses because you can get answers to complex questions without hitting arbitrary limitations. GPT5 comes in different sizes. There's regular GPT5, GPT5 mini, and GPT5 Nano. Each one is optimized for different use cases. Need something fast and cheap? Use GPT5 Nano. Need maximum intelligence? Use regular GPT5. Need a balance? GPT5 Mini is perfect. This gives you incredible flexibility to optimize costs while getting exactly the intelligence you need for each task. If you want to take your AI game to the next level and scale your business with automation, you need to check out the AI money lab. We have 14,000 members sharing strategies, templates, and results. You'll get step-by-step tutorials and over 100 AI use cases. Link is in the comments and description. Now, I've been testing this thing, and I need to show you exactly what happened. I took the exact same prompt that OpenAI used on their website to demo GPT5. It was supposed to create a jumping ball runner game in one shot. Here's what's crazy. GPT5 couldn't get it to work. I

tested it live and the code just didn't function. But when I ran the exact same prompt on Claude Opus 4. 1, it worked perfectly. Created a beautiful playable game right away. Then I tried Sam Alman's example where he said to ask GPT5 to use Beatbot to make a sick beat to celebrate GPT5. This time GPT5 took longer but absolutely destroyed Claude. Created this insane beat maker with controls for humanize, swing, BPM, and you can even mutate the beat. Way more complex than anything Claude made. But here's where it gets interesting. I tested content creation using my favorite SEO prompt for SEO training China. GPT5's title was actually better, but Claude's content felt way more natural and human. Claude was better optimized for search engines, too. GPT5 still has that AI fluff we've been seeing since GPT3. So, here's my honest ranking after all this testing. For the jumping ball game, Claude Opus 4. 1 came first, GPT5 second, Gemini dead last. But for complex interactive apps, GPT5 wins. Now, let me tell you about the coding capabilities. Because this is where GPT5 really shines. It doesn't just write code. It writes good code. Clean, efficient, well doumented code that actually works. This means you can finally use AI to build real business tools without needing a development team. landing pages, internal tools, data dashboards, customer portals. All of this is now possible with just natural language instructions. But here's what really blows my mind. GPT5 can debug existing code and make it better. You can paste in code that's not working, and it will find the problems, fix them, and explain what was wrong. This is like having a senior developer available 24/7 for debugging and optimization. The customer service applications are incredible, too. GPT5 can handle complex customer inquiries, understand context from previous interactions, and provide helpful solutions while maintaining your brand voice. Because it's less likely to make factual errors, you can trust it to give accurate information about your products, policies, and procedures. And it can handle multiple languages fluently, which opens up global markets that might have been challenging to serve before. The data analysis capabilities deserve their own video. GBT5 can process complex data sets, identify trends, create visualizations, and provide actionable insights. That kind of analysis used to require expensive consultants or specialized software. Now it's available to anyone with access to GPT5. The research capabilities are also phenomenal. GPT5 can gather information from multiple sources, synthesize it into coherent insights, and present findings in whatever format you need. Market research, competitive analysis, industry trends, customer behavior patterns. All of this becomes incredibly fast and accurate with GPT5. But here's what I want you to understand after all my testing. GPT5 isn't automatically the best at everything. In my head-to-head test, Claude Opus 4. 1 still won on several tasks. For content creation, I'm sticking with Claude. It feels more natural and human. For coding, Opus 4. 1 is definitely on the same level, sometimes better. There were certain tasks that GPT5 couldn't do that Opus 4. 1 handled perfectly. But a few days ago, this new AI model called Quen 3 thinking dropped out of nowhere from Alibaba. And when I saw the benchmarks, I couldn't believe what I was seeing. This thing was scoring higher than GPT4 on reasoning tasks, higher than Clawude on coding benchmarks, and is completely free to use right now. But here's the thing. I don't trust benchmarks. Anyone can make up numbers and put them in a fancy chart. So, I decided to put this AI through the ultimate test. I created 10 brutal coding challenges. These weren't simple hello world programs. I'm talking about full games, complex simulations, and interactive dashboards. The kind of stuff that separates the real AIs from the pretenders. And what I found will shock you. Let me walk you through exactly what happened when I tested this new Chinese AI against some of the hardest coding tasks I could think of. Test number one was a typing speed game. Complete HTML file with CSS and JavaScript. had to display quotes, show an on-screen keyboard, highlight correct letters, keep score, show words per minute. Plus, I wanted a pastel macaron color scheme because why not make it pretty? Most AIs would crash and burn on this. Too many moving parts, too many details to keep track of. But Quen 3 thinking, it nailed it. The game worked perfectly. Fun to play, beautiful design. My exact feedback was the game really works. It's working typing game and I can say that it is fun to play with and have a nice design. That's when I knew this wasn't just another overhyped AI. Test number two. Duet style game. Test number two was even harder. A full duet style game. Two glowing neon balls rotating around a central point. Player controls with A and D keys. White obstacles falling from the top. Game over when balls hit obstacles. Dark background with smooth animations. This is where most A is start to show their weaknesses. Game physics. Collision detection, smooth animations. It's a nightmare to get right. The result, the neon colors looked amazing. The ball controls worked

perfectly with A and D keys. But there was one issue. No obstacles were spawning so close to perfection. But missing that crucial game mechanic still. The fact that it got 90% of a complex game right on the first try. That's impressive. But wait, it gets even crazier. Test number three, 3D Earth Globe. Test number three was a rotating Earth Globe using 3. js JS sphere geometry with 64 segments realistic earth texture ambient and directional lighting continuous rotation responsive to window resizing anti-aliasing enabled this is graduate level computer graphic stuff most humans couldn't code this without looking up tutorials my reaction when I saw the result this is insane it's so fast and I didn't expect it to make a earth globe to be working but look it's so beautiful a globe rotating and you can adjust its speed and reset a fully functional 3D earth that you in control built from a single prompt in seconds. At this point, I'm starting to realize this. A I might be something special. Test number four. 4D hyper cube. Test number four. Push things into the realm of the impossible. A ball bouncing inside a rotating 4D hyper cube projection. Let me repeat that. A negi d hyperccocker. That's fourdimensional geometry projected into 3D space with realistic physics simulation. This is PhD level mathematics combined with advanced graphics programming. The kind of thing that would take a human programmer weeks to figure out the result. It really creates bouncing ball inside a rotating 4D hyper cube projection. It's amazing. It worked. A 4D hyper cube with bouncing ball physics. I can barely wrap my head around how that's even possible. Now, here's where I want to pause and tell you about something that could revolutionize your business. If you're seeing what this AI can do with complex coding tasks, imagine what it could do for your marketing, your content creation, your customer service, that's exactly what we cover in the AI success lab. Over 14,000 members are already using AI to scale their businesses and save hundreds of hours every month. Link in the comments and description if you want to check it out. Test number five, solar system simulator. But back to the tests because it gets even more mind-blowing. Test number five was a complete solar system simulator. Central Sun, orbiting planets at different speeds and distances, optional moons, orbit paths drawn as circles, responsive canvas, smooth motion, visual labels, and tool tips. This is the kind of project you'd assign to a computer science student for their final project. Multiple object tracking, orbital mechanics, interactive UI, smooth animations. The result, beautiful orbital mechanics worked perfectly. The only issue was the tool tips weren't showing content when you hovered over planets, but everything else was flawless. Test number six, multi-app dashboard. Test number six ramped up the complexity even more. A complete dashboard with time, weather, and news widgets, plus two embedded games, a Galaga style shooter, and a night versus slimes game settings panel for API keys. The whole thing had to work as a single HTML file. This is like building three applications in one. a dashboard, a weather app, and two complete games. Most development teams would need weeks to build something like this. The feedback, the dashboard is so nice. Nice UI working, except I don't put any API, so some of the features isn't working, but it's fine. Uh, fully functional dashboard with working games. The only issue was missing API keys, which is expected since I didn't provide them. Test number seven was the Knight versus Slimes game as a standalone. Player moves left and right. Attacks with spacebar to swing a sword. Slime enemies spawn randomly. Score tracking. Five heart-shaped lives displayed. Game development is notoriously difficult. Player input handling. Enemy AI collision detection. Score systems. Health management. Animation timing. The result. It's working, but it just cannot go above since there is slime up there. But still working. A fully playable game with one minor movement limitation. That's better than most indie game developers manage on their first try. Test number eight, second dashboard. Test number eight was another dashboard challenge. Realtime clock, weather and news panels, buttons, launching separate mini games, settings menu for API keys and preferences. This was testing whether the AI could maintain quality when asked to build similar projects. Would it copy paste code or create something fresh? The feedback tells the story. Basic UI, but the time is accurate, same as the date. I just don't like the design. It's pretty plain, fully functional, but with a more basic design approach. The AI chose function over form this time, which shows it can adapt its priorities. Test number nine, Space Invaders first failure. Now, test number nine was where things got really interesting. A complete Space Invaders clone. Canvas-based graphics. Rows of invaders moving side to side and descending. Movable cannon at the bottom. Missiles firing upward. Score tracking, level progression, collision detection. This is a classic game that

has stumped programmers for decades. Getting the movement patterns right, handling projectile physics, managing game state is deceptively complex. The result, pretty basic and game is not working. There's no ball or everything just blocks. Finally, a failure. The AI couldn't handle the complexity of Space Invaders. It created the visual elements, but the game mechanics didn't work. But here's what's fascinating. Out of 10 brutal tests, I got three perfect successes, five partial successes with minor issues, and only two complete failures. That means eight out of 10 tests produce something functional, even if not perfect. Test number 10, modular architecture, second failure. Test number 10 was the ultimate challenge. A modular arcade game with component-based architecture like React, but for games. Abstract components for player, enemy, game loop, and UI. health systems, enemy spawning, score tracking, HUD display. This requires advanced software architecture knowledge, design patterns, component communication, state management, the kind of stuff they teach in senior level computer science courses. The result, failed this test, just black screen, two failures out of 10 tests. But considering the complexity of what I was asking for, that's still incredible. Now, let me show you the benchmarks that made me want to test this AI in the first place. Looking at the performance charts, Aquen 3 thinking scored 85. 0 on AIME25, which is a mathematics reasoning benchmark. That's higher than GPT4. It scored 66. 0 on Live Code Bench, which tests real coding ability, and 72. 4 on BFCL, which measures tool usage. But here's what really caught my attention. In the Arena Hard Benchmark, which tests conversational AI ability, it scored 56. 0. That's not the highest score, but it's competitive with models that cost hundreds of dollars per month. And this thing is completely free. You can access it right now through Quench Chat. Just go to their website and start using it immediately. No credit card required. No subscription fees, no usage limits that I found. The implications of this are massive. Think about what this means for your business. You now have access to an AI that can build complete applications from simple descriptions, games, dashboards, simulations, interactive websites. That project you've been putting off cuz you couldn't afford a developer, you might be able to build it yourself. Now, something massive just happened in the AI world. I'm talking about GLM 4. 5, and this thing is not playing around. This model is actually performing better than most of the expensive ones. Let me break this down for you. GLM 4. 5 just ranked third globally across 12 hardcore benchmarks behind only OpenAI's 03 and one other model and it's completely open source. Look at these benchmark results. In tool calling GLM 4. 5 gets a 90. 6% success rate. That beats Claude's 89. 5%. It crushes Kimmy K2 at 86. 2% and destroys Quen 3 coder at 77. 1%. In coding tasks, it wins 40. 4% of the time against Claude Son. It beats Kim K 253. 9% of the time and it dominates Quen 3 with an 80. 8% win rate. But here are the real numbers that matter for your business. On SWE Bench verified, which tests real software engineering tasks, GLM 4. 5 scored 64. 2%. That's competitive with Claude 4 Opus at 67. 8% and Claude 4 Sona at 70. 4%. For a model that costs 136 times less on web browsing tasks, GLM 4. 5 gets 26. 4% accuracy on browse comp that crushes Claude 4 Opus at 18. 8% is close to OpenAI's O4 Mini High at 28. 3% but again for a fraction of the cost in the smaller version GLM 4. 5 Air ranked sixth globally still crushing models that cost way more to run. And here's what really got people's attention. Pearl AI posted RIP chat GPT when they saw GLM 4. 5's performance. They said this AI just dropped with 355 billion level power, open- source, and built for reasoning, research, and code. Here's something that blew my mind. Most big AI models need hundreds or thousands of chips. This thing needs eight. That's how efficient it is. GLM 4. 5 uses something called mixture of experts architecture. But instead of making it wider and bloated like most companies do, they made it deeper and more focused, more layers, fewer distractions. The result is better reasoning and more stable behavior. They trained this thing in stages. First 15 trillion tokens of general data, then 7 trillion tokens of specialized code and reasoning data. Then they used reinforcement learning to make it even better at specific tasks. They even built their own training infrastructure called Slime to handle the massive scale. This isn't some weekend project. This is serious engineering. I tested this thing myself. I asked it to build a game. Not just give me code that doesn't work. Actually build a playable game. It delivered. Clean code, no bugs. The game actually worked. And get this, someone actually

built a one-shot Doom clone with GLM 4. 5. A complete game. Working graphics, player movement, enemy AI, all from a single prompt. If you want to learn exactly how to set up these kinds of AI workflows and get access to step-by-step tutorials, you need to check out the AI Success Lab. We have over 14,000 members, and we're constantly sharing new strategies, tools, and tutorials. There are over 100 different AI use cases documented in there, plus you get all the video notes and step-by-step guides. The link is in the comments and description. Now, let me give you specific prompts you can test GLM 4. 5 with right now. Test one, full stack web app. Build me a complete project management dashboard called Project Hub with drag and drop camb board, task creation, due dates and priority levels, team member assignment, progress tracking charts, dark mode toggle, responsive design, and REST API back end with data persistence. Test two interactive game creation. Create a playable space shooter game called Cosmic Defender with player movement, multiple enemy types, power-ups, collision detection with particle effects, progressive difficulty, high score system, and sound effects. Test three, business presentation generator. Create a 15 slide investor pitch deck for SAS start of data sync pro, including problem solution, market analysis, competitive landscape, business model, financial projections, and funding requirements. These aren't basic coding exercises. These are real world applications that would normally take teams of developers weeks to build. GLM 4. 5 can handle all of them. Here are the specific use cases where GLM 4. 5 shines. Content creation. This thing can write blog posts, social media content, email campaigns, and it understands context really well. It's not just generating generic content. It's creating stuff that actually fits your brand voice, coding, and development. If you're building software, GLM 4. 5 can help with everything from writing code to debugging to creating documentation is particularly good at full stack development. Data analysis, need to analyze spreadsheets, create visualizations, generate reports. GLM 4. 5 can handle all of that and it won't break halfway through like some models do. Customer service. You can fine-tune this model on your specific business knowledge and use it to handle customer inquiries. At the cost per token, you could run it 24/7 for less than you'd pay for one human customer service rep. Try these test prompts yourself and you'll see what I mean. The model doesn't just generate code. It builds working applications out there. Here's what I think is really happening. The AI race is changing. It used to be about who could build the biggest model with the most parameters. Now it's about efficiency. How can you get the best performance with the least resources? Chinese AI companies are winning this efficiency game. They're being forced to because of chip restrictions, but that's making them more innovative. Now, let me show you how to actually use this for your business. There are several ways to access GLM4. 5. First, you can use it directly on Z. AI's website. Just go to chatz. ai AI and start using it like chat GPT. It's free to try. Second, you can use their API if you're building applications. The pricing is crazy cheap compared to other providers. Third, and this is the big one, you can download the model weights and run it yourself. It's on hugging face and model scope. If you have the technical skills, you can fine-tune it for your specific use case. Listen, I've been in the AI space for years. I've seen every major release, but what happened has me shook. 10 cent just dropped four AI models that are making OpenAI and Google Panic. They're completely free, open source, and you can run them on your laptop right now. The biggest one, it's competing with OpenAI's 01 mini model. A model that costs you money every single time you use it. And this Chinese model, zero cost. But here's what nobody's telling you. These aren't just good models. They're revolutionary. And I'm about to show you exactly why every business owner, content creator, and developer needs to know about this. 10 cent just released four models 0. 5 billion parameters 1. 8 billion 4 billion and 7 billion. Think of parameters like brain cells more usually means smarter AI. The smallest model runs on your phone. No internet needed, no monthly subscription, no API costs. The 7 billion parameter model competes with models that cost companies millions to train and thousands per month to access. And you can download it right now for free. Now, if you want to get ahead of this AI revolution and learn exactly how to set up models like Hunuan for your business, you need to check out our AI success lab. Inside, you'll find step-by-step video tutorials for deploying these exact models. No technical background required. We've got over 100 use cases with screenshots and walkthroughs, plus 14,000 members are already scaling their businesses with AI automation. You'll get all the video notes, training materials, and daily tutorials. Link is in the comments and description. Don't miss out on this game-changing opportunity. Now, you might be thinking, Julian, this sounds too good to be true, but wait until you see these benchmark scores. On the drop benchmark, which tests reading comprehension, Hunyuan 7B scored 85. 9. That's crushing performance for a free model. On Amy 2024, which tests advanced

math, Hunuan 7B scored 81. 1. And remember, Hunuan is completely free while competitors cost you money every time you use them. But the real kicker, these models support a 256k context window. That means you can feed them 500,000 English words in one go. Open AI's GPT4, maximum 128K context. Hunuan, crystal clear understanding all the way to 256K. Traditional AI models forget stuff. Hunuan connects dots across massive amounts of text and maintains perfect context throughout long conversations. These models support what Tencent calls hybrid reasoning, fast thinking, and slow thinking. Fast thinking is for quick questions, instant responses. Slow thinking is for complex problems. The AI thinks longer to give you better answers. You control which mode to use, think or no think before you prompt. I tested this with a marketing strategy question. Fast mode gave me a response in 3 seconds. Slow mode gave me strategic analysis with market research. Now, how do you actually use these models? Where do you download them? Tencent made this simple. Go to GitHub or hugging face. Search for Hunuan. That's it. Got a smartphone? Use the 0. 5B model. Runs on 4 GB of RAM. Need more power? The 1. 8B model handles complex conversations. Runs on 8 GB of RAM. Running a business? The 4B model delivers professionalgrade performance. Needs 12 GB of RAM. Want maximum power? The 7B model matches performance of models that cost thousands per month. requires 16 GB of RAM, but gives you unlimited GPT4 level intelligence. These models are optimized for agentic capabilities. They're designed to act like AI employees, not just chat bots. I set up the 7B model as my virtual research assistant. I gave it a business question about market entry strategies. It created a research plan and executed each step systematically. But here's the part that's going to make traditional AI companies sweat. efficiency. 10-centent developed angle slim compression. The 7B model compressed with FP8 quantization uses 50% less memory and runs 70% faster with almost no drop in performance. The compressed 7B model runs on just 8 GB of RAM instead of 16 GB. Perfect for most business laptops. Traditional AI companies charge you based on tokens. Every word costs money. With Hanuant, you pay once for the hardware and that's it. No monthly fees, no usage limits, no surprise bills. A typical business using Chat GPT Plus and Claude Pro pays $40 per month. Add API costs and you're looking at $200 to $500 monthly with Huan running locally, zero ongoing costs. A Chinese AI company called Manis just dropped something so crazy that it's making open AI and Google scrambled to catch up. They call it wide research. And I'm not kidding when I say this could change everything about how we use AI. So, what is Manis? Most people haven't heard of them yet. They're this AI company from Singapore. But here's the thing. They've been quietly building what they call a general AI agent. Not just a chatbot, not just another AI tool, a full AI agent that can actually do real work for you. Think of it like having a super smart assistant that never sleeps, never gets tired, and can handle pretty much any task you throw at it. But here's where it gets insane. Everyone else in AI is doing what they call deep research. Open AI has it. Google has it. Even the new AI companies are all copying the same thing. Deep research means one really smart AI agent digs deep into a topic. It spends hours researching, reading everything, writing detailed reports. But Mana said, "Forget that. What if instead of one agent working for hours, we had 100 agents working together for minutes? That's wide research. And it just launched on July 31st, 3 days ago. Here's how crazy this is. Let me show you what they demonstrated. They wanted to research 100 different sneakers. Normally, this would take a person days, maybe weeks. Even AID research would take hours. Maniswide research did it in minutes. They spun up 100 agents, one for each sneaker. Each agent looked at design, pricing, availability, reviews, all at the same time. Then they combined all the results into a perfect spreadsheet and web page. 100 agents working together. like having 100 employees all focused on your one project. But wait, it gets better. Most AI tools let you burn through your credits without warning that then you get a huge bill. Manis builtin credit monitoring. Yeah, it shows you exactly how much you're spending before you spend it and warns you if you're about to go over budget. The batch content generation is mind-blowing. Need 50 social media posts? Done. Need 100 product descriptions? Done. need slides for 20 different presentations, all created at the same time by different agents working together. But here's the part that really gets me excited. Each agent isn't just specialized for one thing. Every single agent in wide research is a

full Manis instance. That means each one can do anything Manis can do. Most multi- aent systems give agents specific roles. One agent is the manager, one agent codes, one agent designs. But if the designer agent gets stuck, the whole system breaks down. Not with wide research. Every agent is like having a full AI employee. If one gets stuck, another can jump in and help. They all work together, but they're all capable of handling any task. The technical stuff behind this is incredible. Each agent runs on its own cloud computer. That's right. When you use Manis, you're not just talking to an AI. You're controlling a whole cloud computing setup through conversation. It's like having access to a supercomput that you control just by talking to it. And unlike other AI agents that might hallucinate or make stuff up, these agents are doing real research, going to real websites, pulling real data, then organizing it exactly how you need it. But the time savings aren't even the best part. It's the cost savings. Running 100 agents for 10 minutes costs way less than running one agent for 10 hours. They showed a before and after. Before Wide Research, a typical task needed five full-time employees working for 2 hours. That's 10 hours of human work. After wide research, one person using Manis can get the same result in 10 minutes. That's 60 times faster. And here's something nobody else is doing. The AI agents actually talk to each other. They have their own protocol for sharing information and coordinating work. Think about it. When you have a team of people working on a project, they need to communicate, share updates, make sure they're not duplicating work. The agents do the same thing automatically. This is why wide research works so well. It's not just 100 agents working independently. is 100 agents working as a coordinated team. Now, I need to be honest with you, this isn't available to everyone yet. Right now, it's only for their pro users who pay $199 a month. That's not cheap, but when you think about what you're getting, it makes sense. You're getting access to what is basically a personal supercomput with 100 AI agents ready to work on your projects available 24/7. They're planning to roll it out to their cheaper plan soon. But if you're serious about using AI in your business, the pro plan might be worth it just for wide research. Here's what I think is really happening. Everyone in AI has been focused on making individual AI models smarter. GPT5 will be smarter than GPT4. Claude 4 is smarter than Claude 3 and so on. But Manis realized something. Maybe the future isn't about one super smart AI. Maybe it's about lots of smart A's working together. And honestly, that makes more sense. Think about how humans solve big problems. We don't rely on one genius. We put together teams of smart people who specialize in different areas. Wide research is like having an instant team of 100 experts, all working on your problem, all coordinating their efforts, all delivering results faster than any single expert could. The practical applications are endless. Market research that used to take months now takes hours. Content creation that required whole teams now happens automatically. Product comparisons that were impossible before are now simple. Julian Goldie reads every comment, so make sure you comment below and let me know what you think about this. Are you as excited as I am, or do you think this is just hype? You could use it to research the top 100 MBA programs worldwide. Each agent takes one program and creates a comprehensive comparison spreadsheet. You could analyze 500 stocks. Each agent looks at financial performance, analyst ratings, and recent news, then ranks them all by investment potential. A marketing agency could create social media campaigns for 50 different clients simultaneously. Each agent handles one client and creates custom posts and content calendars. This is the kind of AI that can actually transform how businesses operate. Not just make things a little faster, actually change what's possible. The infrastructure is already there for any task that can be broken down into parallel work. Imagine batch creating hundreds of videos or analyzing thousands of customer reviews or optimizing hundreds of marketing campaigns simultaneously. But here's what really impresses me about Manis. They're thinking about the user experience. The credit monitoring shows they understand people need to control costs. Now, if you want to stay ahead of these AI developments and learn exactly how to implement tools like wide research in your business, you need to check out the AI success lab. We have over 14,000 members and over 100 different AI tutorials covering every major tool. You get step-by-step guides, use cases, and access to a community of entrepreneurs, all sharing what's working. The link is in the comments and description. But let me get back to wide research because there's more. The fact that this is coming from China is significant. For years, everyone assumed the US would dominate AI. Open AI, Google, Microsoft, all the big names are American. Wide research is different from anything open AI or Google has built. It's not trying to be a better version of Chat GPT. It's solving problems that Chat GBT can't solve. This is the kind of innovation that changes industries. When someone takes a completely different approach and shows it works better. And honestly, I think we're going to see more of this. companies from around the world taking unique approaches to AI. Not just trying to build the biggest language model, but

finding new ways to make AI actually useful. The really smart companies are going to adopt tools like Wide Research early before everyone else figures out how powerful they are. And for content creators, marketers, researchers, analysts, consultants, anyone who does knowledge work, this is gamechanging. The bottleneck has always been time. How much research can you do? How much content can you create? How many options can you analyze? Wide research removes those bottlenecks. Suddenly, you can research 100 options as easily as one. Create 50 pieces of content as fast as one. Analyze hundreds of data points simultaneously. This isn't just about doing the same work faster. It's about doing work that was impossible before. And that's what gets me most excited about AI right now. Not just making existing processes better, creating entirely new possibilities. Wide research is one of those breakthrough moments like when the iPhone showed that phones could be computers or when Google showed that search could be instant and relevant. It's a new category of AI tool and manus built it first. Now here's the question. How are you going to use this kind of technology cuz it's not enough to just know about it. The real advantage comes from actually using it. Start thinking about tasks in your business that could be parallelized. Research projects that require looking at multiple options. content creation that needs variation and scale, analysis that requires processing lots of data. Those are the perfect use cases for wide research. And the sooner you start using it, the bigger advantage you'll have over competitors who are still doing things the old way. Wide research is just the beginning. Manis is building more features on the same infrastructure. Other companies are going to copy this approach and soon having 100 AI agents working for you will be normal. But right now, it's still new. Still gives you an advantage. still makes people say, "Wow," when they see what you can accomplish. That window won't last forever. So, if you're serious about using AI to scale your business, to save time, to get better results, now is the time to act. If you want to get ahead of this trend and start using AI to actually scale your business, you need to check out my AI profit boardroom. This is where I share the latest AI tools and strategies with over 1,000 members who are already using AI to grow their businesses. We keep you ahead of every new development so you never miss an opportunity. Next, if you want to see exactly how we can help implement AI in your business to get more leads and customers, book a free SEO strategy session with my team. We'll show you step by step how to use these tools. The link is in the comments and description. And finally, if you want access to over 100 AI tutorials and use cases, join the AI success lab. You can see the checklist of 100 different tutorials that I give away as freebies every day inside the school feed. plus all the video notes from videos like this one. It has 14,000 members because people don't want to miss out on being part of something this big. Link in the comments and description. Julian Goldie reads every comment. So, make sure you let me know what you think about Wide Research and how you plan to use it. This is the kind of AI update that changes everything. And I wanted to make sure you heard about it first because the future of work is here and it looks like 100 AI agents working together to make you more successful than ever before. That's it for today. Thanks for watching and I'll see you in the next video where we'll be covering more AI breakthroughs that you need to know