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✈️🌎 AI Travel Command Center 🤖🧳
🚀 Building an AI-powered travel SaaS that turns a simple trip idea into an organized, actionable experience.
📍 Plan destinations
🗺️ Build itineraries
📅 Manage schedules
🎒 Track travel details
🤖 Use AI to optimize the journey
All from one intelligent command center. ⚡
Built with 🧑💻 React Native + Expo + JavaScript + modular SaaS architecture.
💡 Plan → 🗂️ Organize → ⚡ Optimize → ✈️ Travel
🚧 Currently building toward production. 🔥
#AI #MachineLearning #LLM #RAG #EVALS #AIEngineering #GenerativeAI #Innovation
The network for creativity
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AI Home Services App — Smart Technician Booking & Repair
A premium AI-powered home services app designed to simplify how people diagnose home problems, find the right service, and book qualified technicians.
The product uses AI to turn a simple problem description into a clear service recommendation — helping users move from “What’s wrong?” → “What do I need?” → “Who can fix it?”
Key Experience
→ AI-powered problem diagnosis
→ Text, voice & photo input
→ Smart service recommendations
→ Technician discovery & booking
→ Transparent service information
→ Real-time technician tracking
→ Premium, intuitive mobile experience
The concept is designed for founders building AI home-service platforms, technician marketplaces, repair apps, and SaaS products where reducing friction and improving the booking experience can create a stronger customer journey.
Role: Product Designer
Focus: AI Product Design · UX/UI · Mobile App Design · Service Marketplace
Building AI for healthcare leaves zero room for error.
I’m currently collaborating with an incredible team on Raphald AI, a medical detection application. Building the systems for a project with stakes this high is a massive reminder that the underlying backend architecture matters just as much as the machine learning model itself.
When integrating diagnostic AI, your API endpoints cannot drop requests, and your database workflows demand absolute integrity. You aren't just passing JSON payloads; you are handling critical, real-time workflows where stability is non-negotiable.
Engineering these systems continues to shape my approach to building robust Python backends. If you are developing a product that requires reliable AI integration or rock-solid FastAPI infrastructure, check out the newly updated services on my profile. Let's build something that works when it counts.
AetherOS is a web desktop for AI agents. The entire project sits inside a single HTML file. It starts with a boot screen. Then it opens into a dark workspace. The background has black animated particles. Everything runs on plain CSS and vanilla JavaScript. The site loads zero external dependencies and zero sound files.
The bottom dock holds seven apps. These tools manage four AI agents: Atlas, Sage, Bolt, and Vega. The Agents app shows them chatting and passing tasks. Files lets you browse a drive. Memory shows a live graph of what the system knows. Users can also modify a history timeline. Studio lets you test prompts. Flows builds node pipelines, and Cost tracks token usage.
Every window can be moved, resized, minimized, or maximized. The top menu bar has a model switcher. It routes tasks to Claude Opus 4.7, GPT-5.2, Gemini 3 Pro, DeepSeek V4, or Llama 4. The OS also has Spotlight search, sound toggles, and local API key inputs. The interface uses dark glass panels with blue highlights.
Building the node graphs in one single HTML5 file was tough. At first, the physics nodes pushed apart too hard. They floated right off the screen. Dragging extra lines while getting zoomed in also threw off the precise positions. I fixed the center gravity math. Then I divided mouse positions by the zoom factor. That stopped the unnecessary movement and kept the OS stable.😊
