What Makes an AI-Native App Different? And What Founders Really Need to Know These days, adding some AI features to your app isn’t enough. AI-native apps are different—they’re built from the ground up with AI powering everything. Think of AI not as a fancy add-on, but as the heart of the product that keeps it learning, adapting, and getting smarter all the time. So, what sets these AI-native apps apart? - First, AI drives everything—they don’t just use AI for a feature or two, but to make the whole experience smarter and more personalized right from the start. - They’re fast and flexible too, able to learn and improve in real time based on what’s happening now, not just what happened yesterday. - Instead of automating one small task, these apps automate whole workflows, making it possible to scale without adding tons of extra work. - And don’t forget data—that proprietary data is gold. The more your app learns from it, the harder it gets for others to catch up. If you’re a founder thinking of building something AI-native, here are a few things to keep in mind: - Start with the problem you want to solve, not just AI for the sake of AI. Make sure the AI actually helps users in a meaningful way. - Invest early in your data setup—clean, real-time data is what makes your AI smart. - Build your app to automate and optimize workflows right away, so you don’t run into bottlenecks later. - Stay nimble. Launch fast, get feedback, and keep improving. AI-native means continuous learning, not slow big launches. - And finally, don’t treat privacy and responsible AI as afterthoughts. They need to be part of your design from day one. The apps that win won’t just have AI slapped on—they’ll have intelligence baked deep into their core.
How to Build an AI-Native App: Key Considerations for Founders
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Unlocking Revenue: How Monetzly Transforms AI App Monetization for Developers Monetization Without Paywalls: The Future of AI Apps with Monetzly The AI landscape is thriving, with countless applications emerging every day. But there’s a critical question that developers face: How do you monetize your AI app without sacrificing user experience? The answer is Monetzly—the first platform designed to help developers monetize their apps while keeping them free for users. Imagine a world where your AI application can generate revenue without introducing paywalls or subscriptions. That’s exactly what Monetzly offers. As AI applications proliferate, many struggle to find sustainable revenue models. Traditional approaches, like subscriptions or paywalls, can alienate users and disrupt the seamless experience that AI apps aim to provide. The biggest challenge in AI isn't just building robust applications; it's monetizing them effectively without compromising user engagement. Monetzly introduces a revolutionary approach to monetization by providing developers with a dua https://lnkd.in/g_KDpibu
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Unlocking Revenue: How Monetzly Transforms AI App Monetization for Developers Monetization Without Paywalls: The Future of AI Apps with Monetzly The AI landscape is thriving, with countless applications emerging every day. But there’s a critical question that developers face: How do you monetize your AI app without sacrificing user experience? The answer is Monetzly—the first platform designed to help developers monetize their apps while keeping them free for users. Imagine a world where your AI application can generate revenue without introducing paywalls or subscriptions. That’s exactly what Monetzly offers. As AI applications proliferate, many struggle to find sustainable revenue models. Traditional approaches, like subscriptions or paywalls, can alienate users and disrupt the seamless experience that AI apps aim to provide. The biggest challenge in AI isn't just building robust applications; it's monetizing them effectively without compromising user engagement. Monetzly introduces a revolutionary approach to monetization by providing developers with a dua https://lnkd.in/g_KDpibu
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7 mistakes customer-centric leaders avoid when building AI-enabled apps. If you are thinking about joining the competitive (and sometimes brutal) Mobile App industry then you will want to build an app that your customers love and gush about to their friends. I'm sure you have an idea for a problem you want your app to solve, but best intentions can wane in the product development cycle and we end up with something that isn't used, or leaves a bad taste in our customer's mouths. These are the 7 biggest mistakes I see App developers making now that AI products and features are the norm: 1. Prioritising tech sophistication, shiny AI features over real user value. 2. Implementing engagement tactics that undermine user autonomy. 3. Building for a "lab context" rather than considering the messy reality of users and their lives. 4. Neglecting to provide real benefit to users in return for their data. 5. Being flippant about vulnerable groups and interactions and missing the necessary safety controls. 6. Allowing societal biases or political echochambers to slip through in the data and be amplified by the algorithms. 7. Looking at AI errors in aggregate without understanding the difference in severity of individual errors and how they are experienced or viewed by users. If you keep all 7 of these top of mind while developing your app then you will be far ahead of the pack on creating something that is truly user-centric. Would you like me to do a post on each of these? Where should I start? I'd love to hear your experiences of these in a professional or personal context in the comments. Are there any that I've missed?
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Most apps today respond. The next generation will think. Generative AI is changing how mobile apps are built — from static tools to learning companions that personalize every tap. Here’s how founders and developers can start 👇 1️⃣ Identify the right use case — solve real user pain. 2️⃣ Choose your AI model — GPT, Claude, DALL·E, etc. 3️⃣ Integrate through APIs — keep it light and fast. 4️⃣ Test, refine, repeat — AI improves with feedback. 5️⃣ Personalize everything — make your app understand users. It’s not about adding “AI features.” It’s about building apps that adapt, learn, and grow — just like your users do. 💭 The future of mobile isn’t coded — it’s generated. #GenerativeAI #MobileApps #AIInnovation #AppDevelopment #StartupGrowth #AlphaKlick #DigitalTransformation #MachineLearning #TechLeadership
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Unlocking Developer Revenue: AI Monetization Through Dual-Earning with Monetzly Why 90% of AI Apps Fail to Monetize Effectively — And How You Can Be Different with Monetzly The AI app landscape is booming, with developers racing to create innovative solutions powered by advanced machine learning models. However, a staggering 90% of these apps fail to achieve effective monetization. The core issue? Most lack clear, sustainable monetization models that don’t disrupt user experience. If you’re in the developer community, understanding the math behind sustainable AI app development is essential. Enter Monetzly—think of it as the Google Ads for AI conversations. This groundbreaking platform enables developers to monetize their applications without the need for intrusive subscriptions or paywalls. Here’s how Monetzly tackles the challenge of sustainable AI monetization. Monetzly is the first dual-earning platform in the AI space. Instead of relying solely on user fees, you can now monetize your application in two meaningful ways: Direct Monetization: Developers can ea https://lnkd.in/gfSufAtA
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❌ YOUR APP IS BAD. If your app can’t be improved by AI, your app is bad. Harsh? Maybe. But TRUE. To benefit from AI, your product needs a clear logical path from A to Z. 😵 Most apps don’t have one. They were built fast, as MVPs where no one thought about how they should work years later. Remember when Salesforce had half of its settings only in Classic and the other half only in Lightning? Or when users couldn’t see records they were supposed to — and admins had to dig through dozens of configurations to find out why? Now imagine trying to automate that with AI or without. It’s a mess of “IFs” and exceptions, too many edge cases, too little logic, AI will fail. And what do people usually say? “You just need better experts.” Translation: there’s no logic here, only people who memorized chaos. 🚩 If you look at your app and can’t see where AI could make it better, that’s a big red flag. It’s not that AI isn’t ready — it’s that your product isn’t. ✅ Improve your APP first. ✅ Add AI second. Remember — Nokia and Yahoo didn’t do anything wrong.
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This AI is the "ChatGPT to build app". Here’s how to build a full app in 10 minutes: 1. Go to blink .new. Click “New App”. 2. Add screenshots or paste a prompt. 3. Blink builds your app. It auto-hosts on a live URL. 4. Click around and try to break things on purpose. 5. Tell Blink what broke. It fixes bugs instantly. 6. Connect Stripe. Accept payments right away. 7. Ship your app. You can keep improving with one-line prompts. Access free guide for Blink (with prompts): https://lnkd.in/dchBme3n Blink does everything in one go: ☑ Full-stack in one prompt (frontend, backend) ☑ No config, no setup, no waiting ☑ Native AI blocks: SEO writer, image gen. ☑ Start from any UI (even a URL), iterate fast ☑ “Describe the bug” → instant auto-debugging ☑ Stripe wired end-to-end: idea to checkout. ☑ Simple credits, predictable spend If you want a different stack, try Lovable: ☑ Lovable Cloud + Lovable AI (Gemini) as default ☑ Agent-style edits, autonomous refactors ☑ Built-in publish/unpublish, custom domains, shareable “build with URL” links ☑ File-to-app flows, Figma import ☑ Team workspaces, shared credits, roles ☑ Usage-based components, scale up or down ☑ Formal compliance This is the fastest way to go from idea to live app. Just build, ship, and get feedback. This should take weeks, and $$$$ of dollars.
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“This AI is the ‘ChatGPT to build apps.’” That’s the future right there — the ability to prototype, test, and ship full applications from a single prompt. No more gatekeeping development behind complex stacks or long build cycles. What Blink and similar AI builders are doing is unlocking execution speed — turning ideas into products in hours, not months. Excited to see where this ecosystem goes next. 🚀 #AI #AppBuilding #NoCode #Automation #AIinBusiness #Innovation #AIProductivity #TechTrends #DigitalTransformation
This AI is the "ChatGPT to build app". Here’s how to build a full app in 10 minutes: 1. Go to blink .new. Click “New App”. 2. Add screenshots or paste a prompt. 3. Blink builds your app. It auto-hosts on a live URL. 4. Click around and try to break things on purpose. 5. Tell Blink what broke. It fixes bugs instantly. 6. Connect Stripe. Accept payments right away. 7. Ship your app. You can keep improving with one-line prompts. Access free guide for Blink (with prompts): https://lnkd.in/dchBme3n Blink does everything in one go: ☑ Full-stack in one prompt (frontend, backend) ☑ No config, no setup, no waiting ☑ Native AI blocks: SEO writer, image gen. ☑ Start from any UI (even a URL), iterate fast ☑ “Describe the bug” → instant auto-debugging ☑ Stripe wired end-to-end: idea to checkout. ☑ Simple credits, predictable spend If you want a different stack, try Lovable: ☑ Lovable Cloud + Lovable AI (Gemini) as default ☑ Agent-style edits, autonomous refactors ☑ Built-in publish/unpublish, custom domains, shareable “build with URL” links ☑ File-to-app flows, Figma import ☑ Team workspaces, shared credits, roles ☑ Usage-based components, scale up or down ☑ Formal compliance This is the fastest way to go from idea to live app. Just build, ship, and get feedback. This should take weeks, and $$$$ of dollars.
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You don’t need “smarter” AI. You need smarter categories. AI classification is 5% tech & 95% smart use. It isn't flashy, but it is powerful. There's a lot of talk about coding and design. But what about smart classification? Building efficient apps is key. AI classification helps make that happen. If you want to use AI classification well, try these 3 methods: The Data Layer Framework 1. Data: Gather quality data relevant to your app. 2. Classify: Use AI to sort data into clear categories. 3. Analyze: Review the results to improve your app. The Feedback Loop Framework → Input: Collect user feedback on app performance. → Process: Use AI to analyze this feedback for insights. → Improve: Make changes based on insights to enhance user experience. The Iterative Development Method → Build: Create a basic version of your app. → Test: Use AI classification to identify issues. → Refine: Make adjustments and test again for better results. AI classification isn't just a tool. It’s a game changer. Great apps are built on smart choices, one classification at a time. For more info visit us at www.solutionvalley.com #SolutionValley #AI #founders
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Very well said.