DEV Community: Deshan Nethmina

N-CODE AI Studio: Turning Website Audits into a Side Hustle with Open-Weight AI

Deshan Nethmina — Fri, 16 Oct 2026 04:59:05 +0000

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.

N-CODE AI Studio: An Open-Weight AI Website Audit Side Hustle

Deshan Nethmina — Fri, 16 Oct 2026 04:52:02 +0000

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.