My recent interaction with Anthropic's new Mythos-class LLM, Fable, revealed its capability to effectively deploy Microsoft Source Manager for reference management, overcoming a significant challenge that previously hindered other LLMs in their automation of Word. Fable's unique approach demonstrated an end-to-end integrated solution, marking a significant advancement in LLM functionality and workflow hyper-efficiency.
The report on Ireland's universities and their adoption of Large Language Models (LLMs) reveals a cautious approach, with three standout institutions: Dublin City University, University College Dublin, and University of Galway, each employing different strategies.
This report analyzes Manus, an autonomous AI agent launched in March 2025 by Butterfly Effect. Despite interest from institutions, searches reveal no campus-wide deployments, yet it has a strong focus on students. Its turbulent ownership remains unresolved at the time the research was concluded (late April 2026).
This report introduces a series on the deployment of institution-wide LLM agreements in higher education across OECD, EU and similar high-performing economies. It highlights the global adoption of OpenAI's ChatGPT Edu, noting significant national agreements in countries like Italy and Estonia, while emphasizing decentralized U.S. adoption at major universities. Future reports will cover additional countries and LLM SaaS systems.
This posting describes how we automated the Zotero citation process in Word documents produced from LLM-generated Markdown. In passing, footnotes were automated also.
This 62-page document with 160 references discusses the geopolitical tensions surrounding the Strait of Hormuz, including Iran's closure impacting global energy flows. It analyzes military and diplomatic strategies for reopening the Strait, assesses coalition capabilities, and outlines challenges and potential solutions. The report is informed by multi-lingual sources, integrating diverse regional perspectives. Unusally it was generated by a team of AIs, with minimal direction from, a human. It is the first thesis-length demonstrator in a series of studies on the actual power of LLMs to write well-researched well-argued documents.
This is a recreational filler with a serious message for teachers struggling to know what to teach and now to teach in an AI world
The next big bottleneck in AI may not be model size or compute. It may be access to grounded, real-world experience. If millions of embodied robots begin operating in homes, vehicles and care settings, they could generate filtered experience traces that improve LLM-plus systems far beyond what internet text alone can provide. Child-size companion robots may be especially important because they open access to a domain that today’s AI models understand badly: children’s language-in-context and everyday micro-social interaction. But this only works if the architecture is privacy-first: central systems should receive distilled updates, not intimate raw detail from children’s lives, except under tightly governed emergency rules.
The recent announcement of the impending closure of the OER Foundation (based in Otago in New Zealand) has drawn attention again to the sustainability (or not) of major OER initiatives and the longevity of OER policies. Acting on this we decided to do one of our rapid AI-assisted reviews of the current state of the OER domain. The results were challenging. For speed, this review initially started with a series of seven questions put to ChatGPT (version 5.2) - which we have used a lot over the last three years for policy anal
The discussion emphasizes the rising use of AI tools like ChatGPT among students to complete assignments, prompting universities to update academic integrity guidelines, albeit slowly. The concept of "tring" is introduced, highlighting how AI can reduce assignment effort significantly. Detection methods are limited, and reassessing assignment processes is crucial to prevent issues with academic standards.
Opening up learning 