This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.
What I Built
I built N-CODE AI Studio, an open-source AI-powered website auditing platform designed to help freelancers, small businesses, and web development agencies understand website performance and identify opportunities for improvement.
Many small business owners struggle to understand technical reports or know which website problems to fix first.
N-CODE AI Studio aims to turn technical website measurements into clear, actionable recommendations using open-weight AI.
The side hustle idea: Freelancers can use this tool to prepare website health assessments and offer professional performance optimization, accessibility improvements, SEO consulting, and website redesign services.
The product is an early-stage prototype. This business concept has not yet been validated with paying customers.
Demo
Application: https://ncode-ai-studio-hf26.vercel.app/
Case Study: https://ncode-ai-studio-hf26.vercel.app/case-study
Verified Lighthouse + Gemma AI Execution:
https://github.com/Deshan217/ncode-ai-studio-hf26/actions/runs/38784847640
Raw Test Evidence:
https://github.com/Deshan217/ncode-ai-studio-hf26/actions/runs/38784847640/artifacts/11998425425
The verified demonstration runs Lighthouse in a GitHub Actions environment and generates recommendations using Gemma 3 through Ollama.
The hosted Vercel application currently demonstrates the interface and case study. Live cloud AI inference and production PageSpeed API quota configuration remain separate deployment improvements.
Code
The project is publicly available on GitHub under the MIT License.
GitHub Repository:
https://github.com/Deshan217/ncode-ai-studio-hf26
The repository contains the Next.js application, audit processing logic, Ollama integration, test workflows, documentation, and reproducible AI testing configuration.
How I Built It
I used the following technologies:
- Next.js and React: Application interface and server-side API routes.
- TypeScript: Typed application and auditing logic.
- Lighthouse: Website performance, accessibility, SEO, and best-practice analysis.
- Ollama: Local inference infrastructure.
- Gemma 3 1B: Open-weight AI model used for verified recommendations.
- GitHub Actions: Automated builds, testing, and reproducible AI execution.
- Vercel: Hosting for the application interface.
How the AI Workflow Works
The application is designed to accept a public HTTPS website URL, collect audit findings, and generate an understandable improvement report.
For the verified demonstration, GitHub Actions runs Lighthouse directly in headless Chrome, extracts website audit findings, and provides those findings to a locally running Gemma model through Ollama.
The AI then generates prioritized recommendations that a developer or website consultant can review.
Real Testing Results
I tested the workflow against the N-CODE STUDIO website:
Website: https://ncodest.com/
The October 15, 2026 Lighthouse run produced the following mobile lab scores:
| Category | Score |
|---|---|
| Performance | 57/100 |
| Accessibility | 78/100 |
| SEO | 100/100 |
| Best Practices | 100/100 |
The Gemma model successfully generated recommendations related to accessibility, semantic structure, accessible labels, contrast, and performance optimization.
These results come from a single lab run and should not be interpreted as ongoing measurements or a guarantee of user experience. AI-generated recommendations require human review.
Challenges I Faced
One challenge was Google PageSpeed Insights API quota limits.
Although local Gemma inference worked, the first end-to-end attempt could not retrieve audit data because the PageSpeed API quota had been exceeded.
To produce reproducible evidence, I implemented a separate GitHub Actions workflow using Lighthouse directly with headless Chrome, without depending on the PageSpeed API.
This workflow successfully completed both a real Lighthouse audit and real open-weight AI inference.
Another challenge is hosting: an Ollama instance running locally is not automatically accessible to a cloud-hosted Next.js application. A secure production AI service is part of the future roadmap.
Why Does Open Innovation Matter?
Open innovation gives independent developers the freedom to experiment, learn, customize, and build useful products without depending entirely on proprietary AI platforms.
Using Gemma through Ollama makes it possible to run the language model under developer control.
For N-CODE AI Studio, this provides several advantages:
- Ability to inspect and modify the AI prompt and integration.
- Flexibility to experiment with different open-weight models.
- Potential to operate local inference without per-request proprietary model API charges.
- Opportunities for community contributions and improvements.
- Greater control over where AI processing takes place.
The complete application is shared under the MIT License.
My goal is to make useful AI-assisted website consulting more accessible to independent developers and smaller businesses.
What's Next?
My future plans for N-CODE AI Studio include a securely hosted AI inference service, better audit reporting, user authentication, rate limiting, audit history, and professional client-ready reports.
I also want to collect feedback from freelancers and small business owners to see whether this tool can become a practical, affordable website consulting product.
Built by N-CODE STUDIO for Hacktoberfest 2026.
Thank you to the open-source community and the developers building accessible AI tools!
Prize Categories
Best Use of Gemma
N-CODE AI Studio uses Google's open-weight Gemma 3 1B model through Ollama to transform real Lighthouse audit measurements into actionable website improvement recommendations.
The model was successfully executed and tested using GitHub Actions, with real inference results and supporting evidence available in the public repository.
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