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
Back to feedPost
Built a machine learning application that classifies messages as SPAM or HAM using Python and scikit-learn. The project includes text preprocessing, feature extraction, model training and evaluation, FastAPI API integration, Docker deployment, and Git/GitHub version control. The application was built and tested locally.
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
Related posts
Rosso Archive — Gallery Website
Rosso Archive is a website for a contemporary art gallery — a calm, editorial space built around a single idea: twenty-four works.
We kept the interface quiet so the images can speak. Monospaced captions, a soft grey palette and generous white space give the archive the feel of a printed catalogue, while small interactions make it feel alive online.
What's inside:
— Index list view with a live preview of the hovered work
— Grid view with a colour-on-hover state for the active piece
— Work detail page with specs, description and related works
— Seamless zoom-in transitions between artworks and screens
Have a project in mind? Let's talk.
Great work, Dmytro!
A strong specialist skill is useless if the wrong system gets invoked. Long-running AI work breaks in boring, expensive ways.
That is not a prompting problem. It is a routing and authority problem.
How I can help you:
I build project routers for teams using multiple AI workflows, agents, or specialist assistants so that they work with each other as a team.
I design source-of-truth resolutions so your agents retrieve the current rules instead of improvising or relying on assumptions or stale chat data.
I create reusable skill registries and specialist handoff logic hardened by determined and dedicated flow states reducing the need for model reliance on its own internal and displaced memory context.
I separate project states with individualized canonical states bound by rules living under essential model instructions and routed properly so unrelated workflows do not contaminate one another, but exist with awareness of the role each project plays, enabling a persistently growing ecosystem to develop organically.
I add governance wrappers, escalation hooks, and recovery mirrors around existing AI stacks so that trust is designed at the root, and variance is eliminated with rule based source archiving and implementation.
Trust means closing your eyes at night and sleeping soundly because your agents got your back. And I can put it all together for you, precision results sourced and undisputed because I got tired of doing things the hard way.
Phillip, agree that routing is where it breaks. In our builds the worst bugs came from an agent pulling an old rule from chat history instead of the current source. Pinning one source of truth per project fixed more than any prompt change did.
AI Chatbot App — 40% Engagement Lift for a Customer Support Tool
Project description:
The client needed an AI chatbot app built from the ground up — no existing design, no reference, just a goal: make it feel human, fast and easy to use. I designed the full experience in Figma — mapping user flows, building wireframes, then crafting a clean high-fidelity UI with a smooth onboarding screen, contextual quick-reply chips and a minimal visual language that keeps focus on the conversation. Delivered with handoff-ready specs and a reusable component library.
Result:
→ +40% user engagement
→ −25% response time
→ Improved customer satisfaction
Wowwwww I like the aesthetics