PORTFOLIO / 2026

Richard
Marks

Educator. Engineer. Building simple AI systems that matter.

Melbourne
CURRENTLY
Computer Science and Systems Engineering Teacher
@ Waverley Christian College
2017 — Present
FOCUS
GoPythonLangchainPostgreSQLTypeScriptReactNext.jsTailwind CSSDocker

Latest Projects

2023 — 2026
2026

Viva

All learning is the result of thinking. This is a web application designed to address the problem of authenticating student thinking outside of the classroom, not just writing. Combining in-class writing baselines with student submitted digital work to create personalised, viva interviews powered by AI at scale. This is an ongoing project that is still in its validation phase.

GoPostgreSQLChiReactTypeScriptNext.js
2026

Forecasting Application

BonTon Bakery

A forecasting model for BonTon Bakery to predict sales and stock needs. The model uses historical sales data, weather data, school holidays and other factors to predict future sales and stock needs.

PythonPandasNumPyMatplotlibFlask
2025

Github Content Toolkit

A CLI tool that uses AI to generate content for Github repositories based on the repositories metadata (README, commits and releases).

GoVectorDBRAGOpenAI
2025

Batch Report Application

Starting as a Python script in 2023 (see below), I converted the project into a functional web application for wider use due to increased demand for the tool. See it here at https://batch.ac

PythonHTMLCSSJavaScriptFlaskLangchain
2024

EduEats

Food ordering system for schools. Responsible for the frontend and backend development, focused on integrating payment with Stripe. Created the booking system for schools to order food for their students.

Node.jsReactTypeScript
2023

Batch Report Script

Batch is an AI-assisted reporting system I designed to solve a data consolidation problem in education. Teachers typically pull information from multiple fragmented sources and manually synthesise it into structured reports. I built Batch to automate that workflow: it ingests structured datasets alongside reference documents, applies LLM orchestration (via LangChain), and generates a full class set of consistent, tone-aligned reports. Turning a 6 hour manual process into a 1 hour automated one. Originally developed as a Python prototype in 2023.

PythonLangchain

Let's Connect

Always interested in new opportunities, collaborations, and conversations about AI, education and technology.

© 2026 Richard Marks. All rights reserved.