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Slime Machine explores intelligence and the relationship with machines from a non-anthropocentric perspective, challenging the dominant cerebrocentric view. At the center is the slime mold, specifically Physarum Polycephalum, an organism that defies the binarism between individual and collective, existing both as a single entity and as a collective. Moreover, this organism is decentralized: the slime mould lacks a central control system; it is a brainless organism and its control is distributed everywhere and nowhere. And for these reasons it was the inspiration for an algorithm that would follow its characteristics.   The algorithm created is a computer vision algorithm, aaardm.py, and is inspired by the behavior of the slime mold.  It uses the Slime Mould Algorithm instead of the classical machine learning model. This algorithm challenges the traditional forms of computing by creating one that seeks to emulate nature. It is a metaheuristic algorithm designed to be decentralized, random, and non-binary, inspired by the non-binary, decentralized nature of slime mould. It combats the brain-centered view of intelligence. The aaardm.py algorithm thrives with small data, adapting through exploration rather than relying on large datasets. This project represents a more inclusive and ecological way of doing technology. It showcases how computing can evolve by learning from nature, highlighting the effectiveness of randomness and decentralized decision-making in solving complex problems.