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Built an AI-powered document application that allows users to upload documents and interact with their content through an AI-powered backend.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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The model isn't the product.
The experience is.
A digital runway built with 3D, motion, and a little bit of controlled chaos.
No templates. No static grids. Just fashion translated into an interactive world.
Would you put this on a fashion brand's homepage?
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.😊
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.
