The ALTA 2024 shared task concerned automated detection of AI-generated text. Large language models (LLM) were used to generate hybrid documents, where individual sentences were authored by either humans or a state-of-the-art LLM. Rather than rely on similarly computationally expensive tools like transformer-based methods, we decided to approach this task using only an ensemble of lightweight “traditional” methods that could be trained on a standard desktop machine. Our approach used models based on word counts, stylometric features, readability metrics, part-of-speech tagging, and an information-theoretic entropy estimator to predict authorship. These models, combined with a simple weighting scheme, performed well on a held-out test set, achieving an accuracy of 0.855 and a kappa score of 0.695. Our results show that relatively simple, interpretable models can perform effectively at tasks like authorship prediction, even on short texts, which is important for democratisation of AI as well as future applications in edge computing.