DEV Community: Luke Ponga

Face Avatar Generator — My Submission for the DEV x Google AI Studio Track

Luke Ponga — Mon, 23 Feb 2026 04:26:22 +0000

This post is my submission for DEV Education Track: Build Apps with Google AI Studio.

What I Built

I built Face Avatar Generator, a lightweight browser app that transforms an uploaded face photo into a clean, stylized avatar. The app uses Imagen inside Google AI Studio to preserve facial identity while generating a polished, social‑media‑ready avatar.

Here’s the core prompt that powers the app:

"Please create an app that takes an image of a face and generates an identical social media avatar using Imagen."

The goal was to create a simple, fast, and fun tool that demonstrates how easily multimodal inputs and generative outputs can be combined into a single workflow.

Demo

Live App:

https://ai.studio/apps/f70cabcc-b531-4d55-b51a-9813f3569d0d?fullscreenApplet=true

Source Code:

https://github.com/lukeponga-dev/Face-Avatar-Generator

Screenshots:

Video Demo:

My Experience

Working through the Google AI Studio track gave me a much deeper appreciation for how quickly you can turn an idea into a working AI‑powered tool. A few things stood out:

Prompt design matters more than expected. Small phrasing changes dramatically affected output quality. Adding clarity and constraints made the avatar generation far more consistent.
Imagen handles identity surprisingly well. It didn’t just apply a filter — it preserved facial structure while generating a stylized avatar.
The workflow in AI Studio is incredibly smooth. Being able to build, test, and deploy in one place made iteration fast and enjoyable.
Safety features are built in. It was reassuring to see the platform automatically handle safety checks.
Even small projects feel complete. Connecting image input → model generation → output display made the app feel polished and usable.

Overall, this track was a fun excuse to build something creative, learn more about Imagen, and ship a working tool quickly.

Avatar Profile Generator Built with Imagen — Build Your Own App

Luke Ponga — Fri, 20 Feb 2026 21:55:46 +0000

This post is my submission for DEV Education Track: Build Apps with Google AI Studio.

TL;DR

I built a lightweight Avatar Profile Generator using Imagen inside Google AI Studio. Upload a face → get a clean, stylized avatar. The whole workflow runs in the browser using React + the Gemini API. Includes demo, source code, prompt, and lessons learned.

What I Built

I created an avatar profile generator that transforms a user‑uploaded face image into a clean, stylized social media avatar using Imagen. The goal was to show how easily multimodal inputs and generative outputs can be combined inside Google AI Studio.

The entire app runs in the browser using React and the @google/generative‑ai client, with a modular service layer that keeps the Gemini integration clean and extendable.

Prompt Used

This is the exact prompt powering the avatar transformation:

"Please create an app that takes an image of a face and generates an identical social media avatar using Imagen."

Keeping the prompt short and explicit helped the model preserve identity while applying a stylized look.

Demo

Live Project:

https://ai.studio/apps/f70cabcc-b531-4d55-b51a-9813f3569d0d?fullscreenApplet=true

Source Code:

https://github.com/lukeponga-dev/Face-Avatar-Generator

My Experience

Working through the Google AI Studio track gave me a much deeper appreciation for how quickly you can turn an idea into a working AI‑powered tool. A few things stood out:

1. Prompt design is a real skill

Small phrasing changes had a huge impact. Adding clarity, constraints, and examples dramatically improved consistency.

2. Imagen handles identity better than expected

It didn’t just apply a filter — it preserved facial structure while generating a stylized avatar. It genuinely understood the face.

3. The app‑building workflow is incredibly smooth

Building, testing, and deploying inside AI Studio felt lightweight and fast. Preview mode made iteration effortless.

4. Safety and guardrails are built in

Seeing safety checks handled automatically gave me a better understanding of responsible AI development.

5. Small projects can feel surprisingly complete

Connecting image input → model generation → output display made the app feel polished and usable.

Support the Project

If you found this useful, you can support the project by:

⭐ Starring the GitHub repo
🔁 Sharing the demo
💬 Leaving a comment or question below

I’d love to hear what you’d build with Imagen or Gemini.

Thanks to the DEV team and Google for putting this track together — it was a fun excuse to build something creative and ship it quickly.

A Pragmatic AI Portfolio Built with Gemini & Cloud Run

Luke Ponga — Sat, 31 Jan 2026 01:10:10 +0000

This is a submission for the New Year, New You Portfolio Challenge Presented by Google AI

About Me

I’m Luke Ponga, a software developer and IT support specialist based in Hamilton, New Zealand.

My background in IT support and systems troubleshooting strongly influences how I approach AI. I build for real users, real constraints, and real workflows—not demos or novelty experiences.

Portfolio

🔗 Live Deployment:

https://portfolio-luke-706599404958.us-central1.run.app/

AI at the Core (What Makes This Portfolio Different)

This portfolio treats AI as a first‑class system, not an add‑on.

Instead of embedding a generic chatbot, I used Google Genkit and Gemini models to build task‑specific AI flows that are:

context‑aware
grounded in real project data
constrained to professional, explainable outputs

AI here acts as a guided assistant, not a black box.

Key AI Features

🤖 AI Project Matcher (Interactive Portfolio Guide)

This feature allows visitors to explore my work using natural‑language queries, such as:

“Show me AI projects”
“What experience do you have with Gemini or Genkit?”
“Which projects are most relevant for backend roles?”

How it works:

Implemented as a Genkit flow with structured context injection
Uses curated project metadata rather than free‑form generation
Responds only within known scope, reducing hallucinations
Optimised for fast, low‑latency responses using Gemini 1.5 Flash

🧠 Career Architect (AI‑Assisted CV & Cover Letter Generator)

Accepts a user‑provided job description
Generates tailored CVs and cover letters in Markdown
Enforces professional tone and structure
Powered by Gemini 2.0 Flash via Genkit

How I Built It

Architecture & Performance

Next.js 16 (server‑first rendering)
Containerised and deployed on Google Cloud Run
Optimised for performance and SEO

AI Engineering

Google Genkit for deterministic AI flows
Strong separation of context, prompts, and formatting
Middleware to reduce hallucinations and enforce tone

What I’m Most Proud Of

This portfolio treats AI as a responsible engineering tool, not a novelty.
It remains usable even without AI enabled.

Technical Details

Repository: https://github.com/lukeponga-dev/portfolio-luke
Language: TypeScript
AI Models: Gemini 1.5 Flash, Gemini 2.0 Flash
Deployment: Google Cloud Run (dev-tutorial=devnewyear2026)

Thanks to DEV and Google AI for encouraging real‑world, responsible AI use.

devnewyear2026 #googleai #cloudrun #nextjs