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.

For the past couple of years, colleagues have been asking me the same questions: “How did you do that so fast?” “What tool did you use for that?” “How are you managing to keep up with all of this?” And every time I explain (the tools, the workflow, the approach) the reaction follows a familiar pattern. First, genuine interest. Then, as soon as I mention code, or the terminal, or VS Code, the shutters come down: “That sounds too complicated.” “I don’t code.” “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. The barrier isn’t ability; it’s exposure. Nobody showed them how to start.

That’s what xKiwiLabs is for.

What follows is a bit of my background and how it motivates this site. To skip the autobiography, jump straight to Why This Site Exists and What’s Coming.

How I Got Here

I’ve been coding since I wrote my first game on my family’s Commodore VIC-20 in the 1980s: BASIC, then C and Visual Basic, then C++, C#, and MATLAB, and eventually Python, TypeScript, and React. But the point isn’t the languages. It’s that 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. Coding wasn’t a side skill; it was the engine that made my research possible.

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, and it gave me an edge.

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 onwards, everything accelerated. ChatGPT, Claude, Gemini, Copilot became genuinely useful general-purpose assistants, not just for research, but for writing, teaching, administration, tool-building, data analysis.

By 2025 the integration was near-total, and now in 2026, it’s total. As I’ve written about in another post, there’s barely a task or hour in my working day where I’m not using an AI tool, an AI agent, or building my own. Writing papers, analysing data, preparing lectures, reviewing grants, managing projects: AI runs through all of it. Not as a gimmick or an experiment, but as basic infrastructure for how I work.

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

I won’t pretend that being a lifelong coder hasn’t helped. It absolutely has. 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. That head start compounded over decades.

But here’s what’s changed: you don’t need that head start anymore.

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 genuinely enjoy it, but all of them gain enough proficiency to be effective. Within weeks, not months, they’re building HTML presentations, writing data analysis scripts, and doing advanced ML and AI work.

The barrier to entry has collapsed. The tools that gave me a decades-long advantage are now accessible to anyone willing to spend a few hours learning the basics. 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. You can get a long way before you ever need to spend a cent.

What used to require years of programming experience now takes curiosity and a willingness to learn. That’s why every academic and student should be paying attention.

Why This Site Exists

When colleagues ask me how I work the way I do, I used to answer one conversation at a time. That doesn’t scale, and honestly, the answers are too long for a hallway chat. What I really need is a place to point people to: somewhere I can share what I’ve learned, document the tools and workflows that work, and update them as the landscape shifts, which it does, constantly.

That’s xKiwiLabs. It 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 genuinely help academics and students work better.

Everything here is free, open-source, and built in the open. The blog posts are my perspective: opinionated, practical, based on what I actually use. The guides are the reference material: step-by-step instructions, prompt templates, tool recommendations. I’ll keep both updated as things change, because they will.

What’s Coming

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 or haven’t known where to start, this site is for you. You don’t need to be a coder. You don’t need to be technical. You just need to be willing to try something new.

There’s a lot more coming soon. But if you’re keen to start improving your workflows, whether as a researcher, teacher, or student, these posts are the best place to begin:

Welcome. Let’s get started.

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

AI Disclosure: This article was written with the assistance of AI tools, including Claude. The ideas, opinions, experiences, and workflow described are entirely my own. The AI helped with drafting, editing, and structuring the text. I use AI tools extensively and openly in my research, teaching, and writing, and I encourage others to do the same. Using AI well is a skill worth developing, not something to hide or be ashamed of.

It’s also worth acknowledging that the AI models used here (and all current LLMs) were trained on vast quantities of text written by others, largely without explicit consent. The ideas and language of countless researchers, educators, and writers are embedded in every output these models produce. Their collective intellectual labour makes tools like this possible, and that contribution deserves recognition even when it can’t be individually attributed.

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