NEW VS Code AI Toolkit 2.0 is INSANE! 🤯 (FREE GPT-5 Inside?)

Today I'm going to show you something that just dropped and it's completely insane. Microsoft just gave every developer free access to GPT5 inside VS Code. I'm talking about the new AI toolkit 2. 0 and this thing is a game changer. You can now run GPT5 claude or any model you want right inside your code editor. But here's the crazy part. You can build AI agents that literally write code for you. I'm going to show you exactly how to set this up in the next 10 minutes. And by the end of this video, you'll have an AI coding assistant that's more powerful than anything you've ever seen. Trust me, this is big. This is revolutionary and it's completely free. Let me show you why this changes everything for developers. And if you stick around to the end, I'll show you how one developer used this to build an entire app in just 30 minutes. So, what exactly is this AI toolkit? is Microsoft's new VS Code extension that turns your editor into an AI powerhouse. Think of it like having Chat GPT, Claude, and every other AI model built right into VS Code. But it's not just for chat. This thing can browse models, test them, fine-tune them, and even deploy them to the cloud. Here's what makes this insane. You're not locked into one AI model. You can use OpenAI's GPT5, Anthropics, Claude, GitHub models, or even run models locally on your machine. That means you control your data, your costs, and your privacy. No more switching between tabs or copying code back and forth. Everything happens right where you're already working. But the real magic happens with agent mode. This is where things get scary good. You can tell the AI agent to do complex tasks like refactoring your entire codebase, adding tests, or even building new features. And it doesn't just give you suggestions. It actually opens files, makes changes, runs tests, and commits code to Git. It's like having a junior developer that never gets tired and works at the speed of light. The timing of this is perfect. Microsoft just rolled out GPT5 Access across their developer tools. That means you're getting the latest and most powerful AI models integrated directly into your workflow. And since it supports multiple providers, you can compare different models side by side to see which one works best for your specific use case. 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. Let me walk you through exactly how to get this set up. First, you need the latest version of VS Code. If you don't have it, just download it from the official site. Once you have that, go to the extensions marketplace and search for AI toolkit. Install it and you'll see a new icon in your sidebar. Now, here's where it gets interesting. You can connect this to any AI provider you want. If you have an open AI API key, plug it in. Same with Anthropic, Azure, or GitHub models. But here's the cool part. You can also run models locally. That means no API costs, no data leaving your machine, and complete privacy. The model browser is where you'll spend most of your time. You can browse hundreds of different AI models, see their capabilities, and switch between them instantly. Want to use GPT5 for complex reasoning? Done. Want to use a smaller, faster model for simple tasks? Easy. Want to use a specialized coding model? They've got those, too. The playground is where you can test different models before using them in your actual code. You can adjust parameters like temperature and token limits and see how different models respond to the same prompt. This is huge for prompt engineering because you can optimize your prompts for each specific model. And speaking of optimizing your business with AI, if you want to scale your business and save hundreds of hours with AI automation, check out my AI profit boardroom at school. com/iprofitlab7462. It's the best place to learn how to use AI to get more customers and grow your revenue. I'll drop the link in the description. Now, let's talk about agent mode. This is where the AI toolkit becomes truly powerful. When you enable agent mode, the AI can actually interact with your development environment. It can open files, run terminal commands, access databases, and even make web requests. This turns it from a simple chatbot into a full-fledged development partner. The model context protocol integration makes this even more powerful. MCP lets the AI agent use specialized tools and APIs to extend its capabilities. Want it to analyze your database? There's a tool for that. Want it to deploy to AWS? There's a tool for that, too. The possibilities are endless. Here's a real example of what this looks like in action. Let's say you want to add CSV upload functionality to your web app. Instead of spending hours coding this yourself, you just tell the agent what you want. It will create the upload form, write the backend logic to process the CSV, add data validation and sanitization, write comprehensive tests, and even update your documentation, all in minutes, not hours. But here's what most people don't understand about AI agents. They're not replacing developers. They're making developers superhuman. You still need to understand

what you're building and make strategic decisions, but all the tedious repetitive work gets handled automatically. The bring your own model feature is a game changer for enterprise developers. If your company has specific security requirements or wants to use proprietary models, you can integrate them directly into the toolkit. This means you get the same powerful interface and agent capabilities, but with complete control over your AI infrastructure. Local model support is huge for privacy conscious developers. You can download models like Code Lama or Star Coder and run them entirely on your machine. No data leaves your computer, no API costs and no rate limits. The trade-off is that local models are usually smaller and less capable than cloud models, but they're perfect for simpler tasks or sensitive code bases. The prompt template system helps you create reusable AI interactions. Instead of typing the same instructions over and over, you can save them as templates and reuse them across projects. This is especially useful for teams where multiple developers need to use the same AI prompts consistently. Fine-tuning support means you can actually train models on your specific code base or coding style. This makes the AI much better at understanding your particular patterns and conventions. It's like having an AI that's specifically trained to work with your team's code. The evaluation tools help you measure how well different AI models perform on your specific tasks. You can run the same prompt through multiple models and compare their outputs for accuracy, speed, and cost. This takes the guesswork out of choosing the right model for each use case. Deployment integration with Azure makes it easy to take your AI powered applications from development to production. You can test locally with small models, then deploy to the cloud with more powerful models when you're ready to scale. Here are some pro tips for getting the most out of the AI toolkit. First, start small. Don't try to have the AI rewrite your entire application on day one. Begin with simple tasks like writing tests or fixing bugs, then gradually work up to more complex features. Second, always review the code the AI generates. These models are incredibly powerful, but they can still make mistakes or hallucinate functionality that doesn't exist. Treat AI generator code like code from a junior developer. It's usually good, but it needs review. Third, use multiple models for different tasks. GPT5 might be best for complex reasoning and architecture decisions, while a smaller, faster model might be better for simple formatting or documentation tasks. The toolkit makes it easy to switch between models. So, take advantage of that. Fourth, secure your API keys properly. Never hardcode them into your source code or share them in screenshots. Use environment variables or the built-in key management features. And remember that cloud AI models send your code to external servers. So be careful with sensitive or proprietary code. Fifth, monitor your costs if you're using cloud models. APIs can add up quickly, especially with powerful models like GPT5. Set up billing alerts and consider using smaller, cheaper models for routine tasks. The security implications of this technology are important to understand. When you use cloud-based AI models, your code gets sent to external servers for processing. For most developers, this isn't a problem. But if you're working on sensitive or proprietary code, you might want to stick with local models or ensure your cloud provider meets your security requirements. Performance varies significantly between different models and deployment methods. Cloud models like GPT5 are incredibly powerful, but have network latency. Local models are faster to respond, but maybe less capable. The AI toolkit makes it easy to experiment with different options to find the right balance for your workflow. The community around this toolkit is growing rapidly. The GitHub repository has examples, starter templates, and community contributions. There's also active discussion in the VS Code community forums where developers share tips, tricks, and use cases. Looking at the bigger picture, this represents a massive shift in how we build software. We're moving from a world where developers write every line of code manually to one where AI handles much of the routine work while humans focus on architecture, design, and business logic. Some developers are worried that AI will replace them. But I think the opposite is happening. AI is making developers more valuable by removing the boring, repetitive parts of coding and letting them focus on creative problem solving and innovation. But here's something most people miss. The real value isn't in writing code faster. It's in writing better code. AI can help you implement best practices, catch potential bugs, and suggest optimizations that you might not have thought of. It's like having a senior developer looking over your shoulder, but one that never gets tired or impatient. The future roadmap for this technology is exciting. Microsoft is investing heavily in AI powered development tools, and we can expect to see more sophisticated agents, better model integration, and even more automation in the coming months. Education and training are crucial for maximizing the benefits of these tools. Developers need to learn how to write effective prompts, understand the capabilities and limitations of different AI models and develop

workflows that leverage AI effectively. The collaborative potential is huge. Imagine entire development teams working with AI agents that understand the codebase, follow coding standards, and can communicate with each other to coordinate complex changes. We're not there yet, but the foundation is being laid right now. Now, if you want to take your AI skills to the next level and learn how to make serious money with these tools, you need to check out my free AI money lab. Inside, you'll get access to over 50 free AI tools and 200 chat GBT SEO prompts that are worth thousands of dollars. You'll learn how to make money with AI agents. Get access to over 1,000 free automation workflows, and see exactly how one member made over $10,000 using Chat GPT. Plus, you'll get a complete blueprint for generating thousands of leads using AI completely free. All right, I'll put the link in the description below. This technology is moving fast and the developers who adapt quickly will have a huge advantage. Thanks for watching and I'll see you in the next