Hacktoberfest 2026 — Week 2: Side Hustle
What I Built
I built N-CODE AI Studio, an MIT-licensed website auditing prototype for freelancers and small-business owners. It combines measurable Lighthouse findings with actionable advice generated by an open-weight AI model. The side-hustle idea: a freelancer can offer a transparent introductory website health check, then optionally offer human-led accessibility, SEO, and performance improvements. I have not validated any revenue or customer demand.
Demo
- Public case-study demo (verified CI results): https://ncode-ai-studio-hf26.vercel.app/case-study
- App: https://ncode-ai-studio-hf26.vercel.app/
- Source: https://github.com/Deshan217/ncode-ai-studio-hf26
- Real AI + Lighthouse CI run: https://github.com/Deshan217/ncode-ai-studio-hf26/actions/runs/38784847640
- Downloadable raw evidence: https://github.com/Deshan217/ncode-ai-studio-hf26/actions/runs/38784847640/artifacts/11998425425
Before publishing: verify the deployed /case-study route responds publicly after the latest Vercel deployment. The original live audit endpoint may still hit Google PageSpeed API quota; cloud Gemma inference is not yet working on Vercel. The working proof is the GitHub Actions run.
How I Built It
The interface is built with Next.js, TypeScript and React. The web-app audit route reads Google PageSpeed Insights Lighthouse lab metrics. The open-weight AI integration uses Ollama and Gemma 3.
To produce reproducible test evidence independent of the PageSpeed API quota, I also developed a GitHub Actions workflow that installs Ollama, pulls gemma3:1b, runs headless Chrome/Lighthouse directly against https://ncodest.com/, and feeds the audit metrics to a locally hosted Gemma instance. This is a real execution, not generated sample data.
The successful October 15, 2026 GitHub Actions run (#38784847640) recorded these single-run mobile Lighthouse lab scores:
| Category | Score |
|---|---|
| Performance | 57 |
| Accessibility | 78 |
| SEO | 100 |
| Best Practices | 100 |
Gemma returned nonempty practical recommendations including review of accessible names, ARIA and page structure, contrast and labeling, and JavaScript/main-thread performance. These are model-generated recommendations to be validated by a developer, not proof of specific fixes. The model also suggested ungrounded pricing; I excluded that from this evidence-based summary.
The complete real Lighthouse JSON and model response are available as the GitHub Actions artifact. GitHub CI installation, automated tests, typecheck and production build also passed in successful runs.
Why Open Innovation?
Running an open-weight Gemma model in Ollama makes the AI recommendation layer inspectable, locally runnable and configurable. Developers can see the prompt logic and run their own tests without relying on a proprietary chat API. The code is shared under the MIT license. This is not a completely offline product: the default web-app measurement layer currently uses Google PageSpeed Insights, while the CI proof uses local Lighthouse CLI.
The Side Hustle
The initial service concept is a website audit report that a small agency or freelancer could produce for a local business, with optional improvements done by a human specialist. The aim is to lower the cost of the first diagnosis, not to replace web developers or falsely promise business outcomes.
Challenges and Lessons
- Unauthenticated Google PageSpeed Insights quotas caused the initial combined test to fail, even while real Gemma inference passed. I addressed the CI demonstration by running Chrome/Lighthouse locally.
- An app deployed on Vercel cannot automatically reach Ollama running on my laptop. Hosted AI requires a separately secured inference service; the current Vercel app is not presented as fully working with AI.
- Lab results vary; one mobile Lighthouse run is not equivalent to actual Core Web Vitals field experience.
- LLM recommendations can contain questionable details, so manual review matters.
What's Next?
Host the AI model behind a secure, authenticated inference endpoint, configure production audit quotas or use a controlled Lighthouse worker, improve abuse prevention and rate limits, collect real user feedback, and develop client-ready branded PDF reporting.
Challenge tags: devchallenge, hf26challenge (confirm these in the DEV challenge editor).
Author checklist before pressing Publish: verify public case-study URL, add a genuine screenshot or short screencast if required by the official template, check eligibility, ensure all required sections/tags are included, and confirm submission deadline. This text is a draft; publication has not occurred.
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