Gemini 3: Build & Automate ANYTHING!

Gemini 3, build and automate anything. Google just gave us control over AI like never before with Gemini 3. I'm talking about features that let you control how AI thinks, sees, and responds. This changes everything about building with AI, and most people are missing it. Today, I'm showing you the four features that separate basic AI users from builders. Let's dive in. All right, so Gemini 3 just launched, and everyone's talking about it, but nobody's explaining what actually changed it. They're just saying, "Oh, it's better. Oh, it's smarter. " that doesn't help you build anything. Let's start with thinking level because this is the biggest one. So, here's what Google did. They realized that not every task needs the AI to think really hard. Sometimes you just need a quick answer. Sometimes you need deep reasoning. Before Gemini 3, every single request got the same level of thinking. Whether you asked it to summarize an email or solve a complex coding problem, it used the same amount of processing power. That's wasteful. It's like using a supercomput to add 2 plus 2. 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. Now, with thinking level, you can control how much the AI thinks before it responds. You got options from lowinking to high thinking. Low thinking is fast and uses less resources. High thinking is slower, but gives you better answers for complex tasks. Think about it like this. If you're building a chatbot that answers simple questions, you don't need high thinking. The questions are straightforward. Give me your hours. Where's my order? What's your return policy? Low thinking handles that perfectly. But if you're building something that needs to analyze complex data or write detailed code or solve multi-step problems, that's when you use high thinking. You're telling Gemini, hey, take your time. Think this through. Give me the best possible answer. And here's what makes this powerful. You can mix them in the same app. Simple tasks. get low thinking. Complex tasks get high thinking. You're optimizing every single request based on what it actually needs. Let me give you a real example. Say you're building an app that helps people plan trips. When someone asks, "What's the weather in Paris? " That's low thinking. It's a simple lookup. But when they ask, "Create a 5-day itinerary in Paris with museums, restaurants, and travel times between each location. " That's high thinking. You need the AI to reason through multiple factors and create a coherent plan. Same app, different thinking levels, better results overall. Now, here's where it gets even better. You can test both levels and see which one works for your use case. Maybe you thought you needed high thinking, but low thinking gives you the same quality. Now, you know, or maybe you're using low thinking and the answers aren't good enough. Bump it up to high. You've got full control. This is the first time we've had this level of control over AI reasoning and it changes everything about how you build. Now, if you want to dive even deeper into AI automation, I've got something special for you. I run a community called the AI profit boardroom. The best place to scale your business, get more customers, and save hundreds with AI automation. Learn how to save time and automate your business with AI tools like Gemini 3. The link is in the comments and description. It's at school. com/iprofitlab. All right, let's talk about the second feature, media resolution control. This one's huge if you're working with images or visual content. Before Gemini 3, when you sent an image to the API, it would process it at full quality every single time. Whether you needed detailed analysis or just a quick check, full quality that's slow, and it uses a lot of resources. Now, you can control the resolution. You can say, "Hey, Gemini, just give me a low resolution. Look at this image. " Or you can say, "I need full resolution. Analyze every detail. " Why does this matter? Because not every image task needs full detail. If you're building an app that sorts images by category, low resolution works fine. Is this a dog or a cat? Is this a landscape or a portrait? You don't need every pixel for that. But if you're building something that needs to read small text in an image or detect tiny defects in a product photo or analyze fine details in a medical image, that's when you use full resolution. You're getting the full picture with all the detail. And just like thinking level, you can mix these in the same app. Quick categorization, low res, detailed analysis, full res. You're optimizing every image based on what you actually need to do with it. Now, let's move to the third feature, and this one's really interesting. Thought signatures. This is brand new, and most people don't even know what it means. So, let me explain. When Gemini thinks through a problem, it has an internal reasoning process. It's working through steps in its head before it gives you an answer. Before now, you couldn't see that process. You just got the final answer. with thought signatures. You can now see how Gemini reasoned through the problem. You can see the steps it took, the logic it used, the decisions it made along the way. Why does this matter? Three big reasons. First, debugging. If Gemini gives you a wrong answer, you can look at the thought signature and see where it went wrong. You can see which step in the reasoning was off. That helps you fix your prompt or adjust your approach.

Second, trust. When you're building AI systems, especially for important tasks, you need to know why the AI made a decision. Thought signatures show you the reasoning. You can verify that it made sense. You can show other people how the AI arrived at its answer. That's huge for transparency. Third, learning. By seeing how Gemini thinks through problems, you learn how to prompt it better. You see what works, you see what doesn't. You get better at building with AI. Let me give you an example. Say you're building an AI that helps people debug code. Someone submits their code and asks why it's not working. Gemini analyzes it and says there's a syntax error on line 12. With thought signatures, you can see the reasoning. Gemini checked the imports first. Then it scanned for syntax errors. Then it found the specific error. Then it verified that was the issue. You're seeing the whole process. That makes the answer more trustworthy. And it helps you improve your prompts for future requests. And here's what's powerful about this. You can use thought signatures to train your team or your users. You can show them, hey, this is how AI thinks through problems. This is the logic it uses. That helps people understand AI better and use it more effectively. It's not just a black box anymore. You're seeing inside the box. Now, let's talk about the fourth feature, and this is the one that brings it all together. Structured outputs with tools. This is massive. So, here's the problem with AI. Sometimes you ask it for something specific and it gives you an answer in whatever format it wants. Maybe you need a list and it gives you a paragraph. Maybe you need JSON and it gives you plain text. You're constantly passing and cleaning up the output. Google fix this with structured outputs. Now, you can tell Gemini exactly what format you want and it will give you that exact format every single time. You want JSON, you get JSON. You want a specific date structure, you get that structure. No more guessing, no more cleaning up messy outputs. And here's where tools come in. You can combine structured outputs with tool use. That means Gemini can call tools and functions and give you back structured data from those tools. Here's why this matters so much. When you're building real applications, you need predictable outputs. You can't have AI giving you different formats every time. You need structure. You need consistency. Structured outputs with tools gives you that you're building reliable systems instead of hoping the AI gives you what you need. That's the power of combining all four features. thinking level for efficiency, media resolution for image handling, thought signatures for transparency, structured outputs with tools for reliability. You're not just using one feature. You're using all of them together to build something powerful. Let me give you some practical tips for actually using these features. First, start with thinking level. Look at every task in your app and ask, does this need high thinking or low thinking? Be honest, most tasks don't need high thinking. Save that for the complex stuff. Second, if you're working with images, default to low resolution first. Test if it works. Only use full resolution when you actually need the detail. Don't just assume you need it. Third, turn on thought signatures for debugging. When something goes wrong, look at the reasoning. See where it broke down. That helps you fix issues faster than just guessing. Fourth, always use structured outputs. If you're integrating AI into an app, don't deal with messy text outputs. Define your structure up front and stick with it. Fifth, if you're doing creative work, set up your character ingredients at the start. Don't try to maintain consistency without them. It won't work. And here's the meta tip. Combine these features. Don't just use one. Think about how they work together. Low thinking plus low resolution for quick checks. High thinking plus full resolution for detailed analysis. Thought signatures plus structured outputs for transparent, reliable results. You're building a system, not just using individual features. Now, if you want to dive even deeper into AI automation, I've got something special for you. I run a community called the AI Profit Boardroom. The best place to scale your business, get more customers, and save hundreds with AI automation. Learn how to save time and automate your business with AI tools like Gemini 3. The link is in the comments and description. is at school. com/iprofit