Building a Mobile App with AI: A Clear File-Based Approach

The big players in artificial intelligence started low. You create an app, a solution, a workflow.AI companies are beginning to push users into significantly higher-priced plans.One that leaves your enterprise dependent, like a stroke patient of breathing machine. Here the full article:https://lnkd.in/eR88RFGc

Today, you can ask #AI: “Build end-to-end observability using OpenTelemetry.” …and it may generate a working solution in under a minute. Impressive. 🏃♂️➡️ ⁉️ But Do you actually understand what AI generated? Do you understand: • Why "traceparent" matters • When sampling matters and when it can hide critical failures • How observability cost scales at production traffic 👶 AI can accelerate implementation. But architecture decisions, trade-offs, and production accountability still belong to you. ✍️ You own every decision AI helps you make. That’s exactly why I wrote this article. ✅ I share a hands-on implementation of end-to-end observability in a Kotlin Multiplatform app, covering not just how to implement it, but why each design choice matters. - https://lnkd.in/gU3PtzEt Use AI to move faster. Stay informed to move wisely. #AI #Observability #OpenTelemetry #Kotlin #KMP #SoftwareEngineering #ServerDrivenUI

Why does every new AI app look exactly the same? Purple gradient. Chat bubble bottom-right. Same three-column layout. It's not a coincidence. It's the default. AI coding agents now scaffold a whole app from a single prompt. That's incredible for speed. But it means thousands of founders are shipping the exact same starting point, the same generic UI, the same look. A product literally launched this month whose entire pitch is fixing the 'every AI web app looks the same' problem. That tells you how real it is. Here's the uncomfortable part: if your app looks like everyone else's, users have no reason to pick you, and no reason to stay. When the UI is commoditized, the UI stops being the moat. So where does the moat go? - Solve ONE painful, specific job better than anyone, not ten things generically. - Push the value below the surface: the workflow, the data, the integrations. - Tie the AI to a measurable outcome, then make leaving expensive (their data, their history, their habits). Generating an app is now the easy part. Building something people can't swap out for the next clone is the work. That's what actually defends your revenue. Building an AI app and worried it looks like everyone else's? DM me "MOAT" and I'll help you find the part worth defending, fixed scope, no fluff. #AISaaS #WebApps #ProductStrategy #BuildInPublic #AIagents

Software interfaces as we know them are becoming obsolete. The shift is already happening. Instead of people using apps manually, AI will operate them. You'll give instructions to an LLM, it opens your platforms, fills forms, clicks buttons. This means the LLM is suddenly more critical than the UI. The future splits software into two layers: a backend that does the work, and a monitoring dashboard for approval and oversight. How do you see your workflow changing when AI handles the repetitive app interactions? #SoftwareEvolution #AI #WorkplaceFuture

Your AI app breaks the moment 50 people upload a PDF. Everything worked fine before that. Single user. Small input. Clean response. Then real usage starts. Multiple users upload files together. And suddenly… Requests start dropping. Processing slows down. Some uploads just fail. From the user’s side? Your product looks broken. But the problem isn’t the AI. It’s how you’re handling files. Most teams treat a PDF upload Like a simple text input. But it’s not. A file needs to be: Stored Parsed Converted into embeddings Processed in memory Now multiply that by 50 users at once. That’s where your system collapses. Not because it’s bad. Because it wasn’t designed for load. I broke down exactly why this happens and how to fix it properly (Swipe before your uploads start failing in production) If your system processes files directly in the main flow, You don’t have scalability. You have a bottleneck waiting to happen. Comment “QUEUE” if you want the exact setup we use.

Your AI app worked perfectly all morning. Hundreds of requests. Zero issues. 💥💥💥 Then 6 PM hits. Suddenly — blocked. Error. Nothing works. 😤 Day 13 — RPD (Requests Per Day) RPD = the total number of times you can call an AI in one full day. Think of your mobile data plan. 📱 You get 2GB per day. Use it all by afternoon? Internet stops. Doesn't matter how urgent it is. AI works exactly the same way. Free plan = 100 requests per day. Paid plan = 10,000+ requests per day. Hit the limit at midnight? It resets automatically. Fresh start. 🔄 Every developer building AI apps needs to plan for this. Otherwise your app dies every evening. 💀 Day 14 dropping tomorrow 👀 Follow so you don't miss it 🚀 #AI #RPD #LLM #APILimits #MachineLearning #LearnAI #Day13 #AIForEveryone #SmallerEffortsBiggerResults #Tech #GenerativeAI

Adding AI into an app sounds cool… Until you realize: the developer pays for every single prompt users send. More users = more API cost. That’s why most AI apps eventually push: subscriptions, ads, or data collection. While building Beyond, I didn’t want users to pay for AI indirectly. So I changed the model. Instead of me buying AI for everyone, users can simply paste their own Gemini or Grok API key. Free plan works surprisingly well for individual use. So now: • AI stays integrated • App stays free • No ads • No subscriptions • Better privacy • Better scalability Sometimes innovation is not adding complexity. It’s removing unnecessary dependency.

I just watched one AI app ask another AI app for help. Codex took over my computer. It opened Claude Code. Asked it a question. Pulled the answer back. Then used that answer inside Codex. That sounds like a gimmick until you realize what just happened: AI did not just answer me. It operated across tools, moved context between systems, and turned another AI into part of the workflow. This is the shift people are still underestimating. The future is not "which chatbot gives the best answer?" It is: Which systems can safely coordinate work across your actual environment? Your files. Your apps. Your tools. Your browser. Your operating context. That is where this gets both powerful and uncomfortable. Because once AI can move between tools, the real bottleneck stops being prompts. It becomes architecture. Permissions. Memory. Audit trails. Approval gates. Clear boundaries between "suggest" and "act." Wild time to be alive.

AI brings a new type of project challenge: AI scope creep. I'm confident. This post is ready, this code is ready to ship, this document is ready. Yes! One final check and I'm ready to ship. Now I'm asking AI if we're good to ship. And the answer is: almost, but there are a ton of issues. A little tweaking there, some fixing here. The AI is talking to my perfectionist side, and my perfectionism is online and listening. When I turned my mobile app Sales Catalyst over to agentic development, I thought I needed to code just a few functionalities before shipping it, some fixes on the admin side, and then ship it. Looking at the backlog, that's not how the past months have gone. Instead of those three additions, I've now built 4 apps instead of one, including a health dashboard. I've moved all code to IaC, added all automated tests, and written a ton of documentation. Just to name a few tasks. While each single task is quite small, the empowerment to build a lot, fast, drives bloated scope and budgets. The AI will never give you the validation that your product is ready to ship. That decision still remains with humans. How do you decide when something's actually ready to ship? #AgenticAI #ScopeCreep #BuildInPublic #ProductDevelopment

How much does it actually cost to build an AI-powered app? Most founders underestimate this by a wide margin. Typical AI app budgets fall between $60K and $150K — and the range depends almost entirely on three cost drivers: data, ML models, and integration complexity. Data is often the most underestimated cost. Collecting, cleaning, labeling, and structuring training data can consume a significant portion of the budget before a single model is trained. ML model development — whether using pre-trained models, fine-tuning, or building custom models from scratch — is the second major driver, and the gap between these approaches in cost and timeline is substantial. Integration is the third major factor: connecting AI capabilities to existing systems, APIs, and infrastructure adds real engineering time that's easy to overlook in early estimates. Swipe through for the full budget breakdown. #AIDevelopment #AppBudget #PricingAndTimelines #AppDevelopment #AppMakersUSA