This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.
What I Built
I built SkinSense, a personalized skincare companion designed to make skincare decisions easier to understand.
The idea came from a real problem: choosing skincare products is confusing. People often struggle to understand their skin type, compare products with similar ingredients, build a routine, and figure out what different ingredients actually do. Many end up relying on random internet searches and trial and error.
I wanted to bring these activities into one simple application.
SkinSense currently includes a frontend with 14 pages and backend API flows for profile management, routine generation, ingredient explanations, and product checking.
The commercial direction is to make SkinSense useful for independent skincare consultants and small beauty studios. They could use a tool like this to organize client-provided information, explain ingredients in simpler language, and prepare personalized skincare education materials more efficiently.
The potential business model is a paid professional workspace. The idea is to save repetitive work while helping consultants provide clearer explanations to their clients. This business model still needs validation with real potential customers.
SkinSense is a skincare education and decision-support tool, not an AI dermatologist. It does not replace professional medical advice or guarantee that a product will work for everyone.
Demo
GitHub Repository:
https://github.com/aniket937/SkinSense
Live Demo / Video:
[Add your deployed application URL or demo video here.]
The demo should show the profile workflow, routine generation, ingredient explanation, and product-checking experience.
Code
Repository: https://github.com/aniket937/SkinSense
The frontend uses Next.js 16, React 19, and Tailwind CSS v4. The backend uses FastAPI and Python 3.13.
The current prototype has been built with separate frontend pages and backend API contracts so the main user flows can be developed and tested independently.
How I Built It
I used Next.js, React, TypeScript, and Tailwind CSS v4 for the frontend, with FastAPI and Python 3.13 on the backend.
The current application includes workflows for:
- Creating and managing a user profile.
- Generating a skincare routine.
- Explaining skincare ingredients.
- Checking product information.
- Handling backend responses through structured API contracts.
The frontend production build passes, and the current backend test suite includes basic health and secret-handling checks.
One important limitation is that the current AI endpoints still use deterministic fallback responses for demonstration. A live open-weight model integration remains an important engineering step, and OCR processing is currently unavailable. I have kept these limitations explicit rather than presenting simulated results as live AI output.
My intended architecture places an open-weight model behind a replaceable backend adapter. The goal is to use it for contextual routine generation, ingredient explanations, product interpretation, and personalized question answering, with structured output validation and safety controls surrounding the model.
Why Does Open Innovation Matter?
Skincare information can involve personal concerns, routines, product histories, and conversations. I believe the architecture should give developers meaningful control over how that information is processed.
An open-weight AI approach could make it possible to:
- Run inference locally or in a controlled environment when practical.
- Evaluate and replace models without rewriting the entire application.
- Customize prompts and behavior for a specific skincare workflow.
- Reduce dependence on a single proprietary model provider.
- Explore different cost and deployment options as the product grows.
Open AI does not automatically make an application private, safe, or free to run. Those benefits depend on the actual model, runtime, hosting arrangement, and application security.
For SkinSense, the aim is to combine open-model flexibility with reliable data handling, clear uncertainty, and safety validation.
The live open-weight model integration is not yet complete in this prototype, so these are intended architectural benefits rather than verified results from a deployed model.
My Agent Session
I used OpenCode to help with development, implementation planning, and debugging.
DevRelay agent session: [Add the actual shareable session link if available.]
My First User
The idea was inspired by a real person's confusion around skincare routines, product selection, and ingredient information.
I have not confirmed a paying customer or a validated willingness to pay for the professional version yet. The next step is to put the workflow in front of independent skincare consultants, gather feedback, and learn which parts genuinely save them time.
Top comments (0)