xKiwiLabs xKiwiLabs

Welcome to xKiwiLabs

Why I started this site, how AI tools transformed my academic workflow, and why I think every researcher and student can do the same, regardless of their technical background.

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This site has been a long time coming.

Since about 2023, colleagues have been asking me the same questions: “How did you do that so fast?” “What tool did you use for that?” And every time I explain (the tools and the workflow) the reaction follows a familiar pattern. First, genuine interest. Then, as soon as I mention code or the terminal, the shutters come down. “That sounds too complicated.” “I wouldn’t know where to start.”

I get it. But I also know it’s not true. Every one of those colleagues is more than capable of working this way. So are their students. Nobody showed them how to start.

xKiwiLabs is my attempt to show them how.

To skip my background, jump straight to Why This Site Exists and Where to Start.

How I Got Here

I’ve been coding since I wrote my first game in BASIC on my family’s Commodore VIC-20 in the 1980s. From there I moved through C and Visual Basic, then C++, C#, and MATLAB, and eventually Python, TypeScript, and React. Coding has been part of how I think and work for as long as I can remember.

That background has been central to my academic career. From early VR work in the late 1990s through my PhD and beyond, I built the research applications, real-time sensing systems, computational models, and data analysis pipelines my work depended on, often the software that collected the data, analysed it, and generated the figures for the papers.

For most of my career, that was unusual in my field. Psychology and cognitive science aren’t traditionally coding-heavy disciplines; most of my colleagues used SPSS, Excel, and PowerPoint. I was the one people came to when they needed something automated or custom-built.

AI Didn’t Start in 2023

My use of machine learning and AI in research goes back well over a decade: pattern recognition in behavioural data, classification of movement dynamics, pose detection and human movement analysis, computational modelling of human coordination, and early NLP tools for text analysis, long before the current wave of generative AI.

In 2019, I started working more closely with Mark Dras from Computing at Macquarie, just as BERT and the transformer models that followed were rapidly improving. I began integrating them into research workflows: text classification, semantic analysis, automated coding of qualitative data. Then, from 2023, ChatGPT, Claude, Gemini, and Copilot became general-purpose assistants I could use beyond research, for writing, teaching, administration, tool-building, and data analysis.

As I’ve written about in another post, there’s now barely a task or hour in my working day where I’m not using an AI tool, an AI agent, or building my own. I use them to write papers, analyse data, prepare lectures, review grants, and manage projects.

The Coding Advantage, and Why It’s No Longer a Barrier

Being a lifelong coder has helped. Knowing how to code meant I could automate repetitive tasks, build custom tools, process data at scale, and integrate systems together, long before AI made any of that easier.

You no longer need to know how to code to work this way.

The current generation of AI coding assistants (GitHub Copilot, Claude, ChatGPT) means that someone with zero programming experience can describe what they want in plain English and get working code back. I’ve seen this firsthand with my students (undergraduates, honours, Master’s, and PhD students), most of whom had never written a line of code before. Some take to it with practical acceptance, others enjoy it, but all of them gain enough proficiency to be effective. Within weeks, they’re building HTML presentations, writing data analysis scripts, and doing advanced ML and AI work.

Learning the basics takes a few hours, and you can start for nothing. VS Code is free. GitHub Copilot is free for academics and students. Most foundation models (ChatGPT, Claude, Gemini) have free tiers that are often all you need to start, and if you’re concerned about privacy you can run models locally with Ollama or LM Studio, both also free.

Why This Site Exists

Answering colleagues’ questions one conversation at a time didn’t scale, and the answers were too long for a hallway chat. I wanted a place to point people to, where I could document the tools and workflows that work.

xKiwiLabs started back in the 2000s as my studio identity for side projects, building research applications and tools for colleagues. I’ve revamped it as a platform for sharing the AI-assisted workflows, tools, and guides that I think can help academics and students with their research and teaching.

Everything here is free and open-source. The blog posts are my opinions, based on the tools I use every day. The guides hold the step-by-step instructions, prompt templates, and tool recommendations. I’ll keep both updated as things change.

Where to Start

If you’re a researcher, academic, or student who’s curious about how AI tools can fit into your work, but you’ve been put off by the technical barrier, this site is for you.

These posts are the best place to begin:

Welcome.

Michael Richardson Professor, School of Psychological Sciences Faculty of Medicine, Health and Human Sciences Macquarie University

AI Disclosure: I wrote this article with help from AI tools, including Claude. The ideas and the workflow described here are my own. The AI helped me draft and edit the text. I use AI throughout my research and teaching, and I encourage others to do the same. Using AI well is a skill to be learned openly.

The AI models used here, like all current LLMs, were trained on vast quantities of text written by others, largely without explicit consent. Every output they produce draws on the ideas and language of countless researchers and writers. Their labour makes these tools possible, and I want to acknowledge it even where no individual can be credited.

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