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✍️ Using AI Without Exploiting Creativity
AI can make us faster, but speed is not the same as integrity. If we want AI to strengthen our work and our reputation, we have to prove that we can use it without becoming careless with other people’s ideas, labour, and intellectual property. ------------- Context: The Quiet Tension Under AI Adoption ------------- Many teams feel two pressures at the same time. One pressure is competitive, we need to move faster, produce more, and keep up with a world where AI is raising output expectations. The other pressure is ethical and cultural, we do not want to become the kind of organization that treats creative work as disposable fuel. This tension shows up in everyday decisions. Should we feed a competitor’s article into a model to summarize it. Can we train internal systems on client documents? Is it okay to generate a “style match” for a brand voice that resembles a specific creator. Should we use AI to produce imagery that looks like an artist’s work? Often, people are not trying to do the wrong thing. They are trying to do the work and do it well, but the norms are unclear. Legal guidance can be complex and evolving, and people fall back on two unhelpful extremes. Either everything is fine because “AI does it,” or nothing is safe so we avoid AI entirely. Neither extreme builds confidence. The path forward is practical ethics, clear standards, repeatable habits, and respect for creators as a core value, not a compliance checkbox. We can use AI powerfully while still acting like the kind of team others want to trust. ------------- Insight 1: “Can We?” and “Should We?” Are Different Questions ------------- AI makes it easy to do things that used to require effort. That ease tempts us to treat capability as permission. But capability is not the same as legitimacy. The question is about technical possibilities. The question should be about impact, consent, and trust. Teams that ignore the should we question move fast in the short term and pay later in reputation, relationships, and internal culture.
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✍️ Using AI Without Exploiting Creativity
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Where are you using AI?
Where are you using AI, or learning AI to implement, right now? If it's somewhere else, let me know in the comments
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Where are you using AI?
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You know what’s crazy?
How many people think if they just don’t deal with something… it’ll magically work itself out. It never does. That conversation you’re avoiding? It doesn’t get easier next month. It gets heavier. Now there’s more emotion attached. More resentment. More fallout. That decision you’re putting off in your business? It doesn’t get cheaper. It gets more expensive. More money lost. More time wasted. More energy drained. Avoidance feels good for about five minutes. It gives you temporary relief. But you’re not eliminating the cost. You’re just adding interest. And here’s the part people don’t want to hear… Every time you avoid something, you train yourself to hesitate. Every time you face it, you train yourself to lead. The difference between people who win big and people who stay stuck isn’t intelligence. It’s not resources. It’s not even confidence. It’s speed of truth. Winners look at the ugly numbers. They have the uncomfortable conversation. They fire the wrong hire. They fix the broken system. They say what needs to be said. Not because it feels good. But because they know delay compounds pain. So if there’s something sitting in the back of your mind right now... that thing you keep saying “I’ll deal with it later”... that’s probably the thing you need to handle first. Discomfort now builds momentum. Avoidance builds debt. Your choice.
A practical framework for building MVPs with Lovable
After building multiple MVPs with Lovable, I realized something important: Building MVPs is no longer limited by coding skills. It’s limited by how clearly you define the product. AI can build almost anything. But only if you give it the right structure. Here’s the exact framework I follow: 1. Start with real problems I don’t start with ideas. I start with problems. I explore Reddit, Discord, and YouTube comments to see what people are struggling with. This helps ensure I’m building something useful and relevant, not just something interesting to me. When the problem is clear, everything else becomes easier. 2. Create a clear blueprint using a PRD prompt Before building, I use Lovable to generate a PRD (Product Requirements Document). This helps define: - Pages and routes - User flow - Core features - Product structure This step is critical. It acts like a roadmap and removes confusion during development. Without structure, AI produces inconsistent results. With structure, it produces production-ready output. 3. Build the skeleton first Next, I generate the basic UI structure: - Layout - Navigation - Pages I don’t worry about features yet. The goal is to create a clean foundation that can be extended easily. 4. Add features incrementally I add one feature at a time using focused prompts. This keeps the system stable and easier to debug. It also helps Lovable produce more accurate results. Trying to build everything at once usually creates messy output. Incremental building works much better. 5. Add authentication Once the core product works, I add authentication using Supabase. This includes: - Login and registration - Password reset - Email verification - Protected routes This makes the product ready for real users. 6. Deploy and iterate Finally, I deploy the product. You can deploy directly from Lovable or connect GitHub and deploy via Vercel or Netlify. Once live, I improve the product based on real usage and feedback.
A practical framework for building MVPs with Lovable
AI for Social Selling on LinkedIn
Hi friends, I'm leveraging social selling on LinkedIn in the pharma industry, and it's going really well. The next step is to leverage and grow my outreaches and my posts without losing any quality. What tools or systems have you put in place for that?
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