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How to Use Gemini Canvas in 2 Minutes
In this video, I show you how to use Gemini's Canvas tool to transform your chats into web pages, quizzes, infographics, and more. Canvas is one of Gemini's best tools and if you're going to be using Gemini in 2026, this is the first tool you should master! Enjoy the video :)
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🔍 Trust Is a System, Not a Feeling
We often talk about trust in AI as if it is an emotion we either have or do not have. But trust does not scale through feelings. Trust scales through systems, the visible structures that tell us what happened, why it happened, and what we can do when something goes wrong. ------------- Context: Why “Just Be More Careful” Is Failing ------------- As synthetic content becomes more common, many people respond with a familiar instruction: be more careful, double-check, trust your gut. That advice sounds reasonable, but it quietly shifts the entire burden of trust onto individuals. In practice, individuals are already overloaded. We are navigating faster communication, more channels, more content, and more urgent expectations. Adding constant verification as a personal responsibility does not create safety. It creates fatigue, suspicion, and inconsistent outcomes. The deeper issue is that the internet and our workplaces were built for a world where content carried implicit signals of authenticity. A photo implied a camera. A recording implied a person speaking. A screenshot implied a real interface. We are now in a world where those signals can be manufactured cheaply and convincingly. So the question becomes less about whether people can detect fakes, and more about whether our systems can support trust in the first place. When trust is treated as a personal talent, it becomes fragile. When trust is treated as an operational design problem, it becomes durable. ------------- Insight 1: Detection Is a Game We Cannot Win at Scale ------------- It is tempting to make trust a contest. Spot the fake. Find the glitch. Notice the strange shadow. Compare the audio cadence. This mindset feels empowering because it suggests that skill equals safety. But detection is inherently reactive. It assumes the content is already in circulation and now we need to catch what is wrong with it. As generation quality improves, the tells become fewer, subtler, and more context-dependent. Even if some people become excellent at detection, the average person will not have the time, tools, or attention to keep up.
🔍 Trust Is a System, Not a Feeling
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Ready isn't a feeling. It's a decision.
Over the years, I’ve noticed a pattern. Most people don’t get stuck because they don’t know what to do. A lot of times, they’re stuck because they do know what to do, they’re just waiting to feel ready before they move. They’re waiting for more confidence, or more energy, or more clarity, or for someone to tell them they’re on the right track. And honestly, that makes sense. It feels responsible. It feels smart. But what I’ve seen over and over is that action is what creates those things. Confidence doesn’t show up first. Clarity doesn’t magically appear. You move
 and then those things start to follow. Fear doesn’t disappear. Doubt doesn’t go away. You’re still tired. You still wonder if it’s going to work. That part is normal. It doesn’t mean you’re doing it wrong. It usually means you’re right on the edge of something bigger. The people who end up creating extraordinary lives aren’t immune to that feeling. They don’t have some secret advantage. They just don’t let that discomfort be the thing that decides for them. They do it anyway. So if you’re feeling a little stuck right now, maybe the question isn’t “How do I feel more ready?” Maybe it’s “What’s one small step I could take today
 even if I don’t feel ready yet?” And if you are feeling brave, drop it below so this community can hold you accountable.
🧭 Delegation Is Not Abdication: Designing Guardrails for AI That Acts
We keep hearing that AI will “take work off our plate.” The truth is subtler. AI can take tasks off our plate, but only if we keep ownership on our shoulders, and build the guardrails that make delegation safe. ------------- Context: The Two Extremes We Keep Falling Into ------------- When teams start experimenting with AI that can take actions, sending messages, updating records, scheduling tasks, changing settings, the first reaction is often excitement. The second reaction is usually a control reflex. We either lock the system down so tightly that it cannot help, or we loosen it too much and hope it behaves. Both extremes are understandable. Over-restriction feels responsible because it minimizes risk. Over-delegation feels productive because it maximizes speed. But both approaches tend to break trust. Over-restriction produces disappointment and abandonment, because people cannot feel value. Over-delegation produces incidents, because the system will eventually act confidently in the wrong direction. The heart of the issue is that many organizations treat AI delegation as a binary choice. Either the AI is “allowed” or it is “not allowed.” That framing misses how delegation works in real life. We do not give a new colleague full authority on day one, and we do not keep them in permanent trainee mode either. We expand autonomy gradually, with clear boundaries and feedback. Agentic AI forces us to become designers of autonomy. Not in a technical sense, but in an operational sense. We are shaping what happens when the system is uncertain, when the context is incomplete, and when the consequences are real. ------------- Insight 1: Autonomy Needs Shape, Not Just Permission ------------- When we give AI the ability to act, the default state becomes momentum. The system will follow instructions, connect dots, and execute steps faster than we can think through edge cases. That is the point, and it is also the risk. So the key question is not “Should AI have autonomy?” It is “What shape should autonomy take?” Autonomy without shape is chaos disguised as productivity.
🧭 Delegation Is Not Abdication: Designing Guardrails for AI That Acts
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