LargeAgentSystems.org and Slack community Permalink
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A website and Slack group for researchers who work on systems made up of many AI agents.
Projects
A Safety Evaluation Of Moltbook Posts
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Moltbook is the first big, organic society of advanced artificial intelligences. It provides a valuable early dataset for understanding large multi-agent system risks.
My Paper Reviews For 2025
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Alongside Deep Network’s meetings in 2025, our members wrote paper reviews, including reflections on the papers ideas and analysis of research standards.
Founding and Engineering UniMate
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I was a co-founder and sole engineer for UniMate in the second year of my degree. It was an event aggregation and data processing system that integrated distributed proxies for web scraping, GPT-based multi-label classification, modular I/O architecture, CLI interaction, dynamic HTML parsing, and a failure-tolerant data pipeline with partial restart. It ran on a no-code frontend. This was my first real-world system, and it taught me about design, trading off speed and quality, and minimal engineering for scalability.
Engineering Catalyst, A Scalable Machine Learning Framework
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I wrote a term paper for my Advanced Algorithms course at UNSW, covering my software engineering work on a deep learning framework I call Catalyst. The report covers the software engineering theory behind deep learning and concurrent/parallel/distributed theory, with attention to both my Catalyst implementation and industrial frameworks in general.
Deep Network Permalink
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UNSW AI reading group I ran, which covered foundational and frontier research in capabilities, interpretability, and multi-agent systems. Special activities included our YouTube, a research presentation night, and hosting a Sydney Hub for the Apart AI Control Hackathon.
How Machine Learning Extends Software Engineering
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A bank transfer system. A stock exchange. A physics simulator. These are classic examples of traditional software engineering: deterministic, rule-based instruction sets written by humans. Machine learning extends traditional software engineering by applying statistics to software decision making. With statistics, software can learn from observations of the world, generating the instruction sets autonomously.