I've been working on a native Swift app to help me keep up to date with my RSS feeds, I'm really happy with the visualisation above that shows me the velocity of collections of RSS feeds (I group my RSS feeds by broad theme).
I can now pass a grouping to an LLM to get a summary, and I'm choosing the time window based on the broad velocity of the feed grouping. I can also ask for a detailed, intermediate, or summary analysis.
Below is the summary from the last run from my scholarly publishing digest:
# Scholarly Publishing
**Date: 5 August 2026**
## Overview
Recent developments in scholarly publishing reflect a significant intersection of artificial intelligence (AI), funding initiatives, and evolving publication practices. Key themes include the integration of AI in research, the challenges of traditional publishing models, and the emergence of new collaborative frameworks. This briefing synthesizes insights from various sources to highlight these trends and their implications for the future of scholarly communication.
## 1. AI Integration in Research and Publishing
### Wiley's Involvement in the Genesis Mission
Wiley has joined the U.S. Department of Energy's Genesis Mission Consortium, aimed at enhancing AI-powered scientific discovery. This initiative, likened to the Manhattan Project, seeks to fund innovative projects that apply AI to critical scientific challenges. Wiley's role as the only scientific publisher in the consortium underscores its commitment to integrating AI into research workflows, providing tools and resources to enhance scientific productivity ([Journalology](https://newsletter.journalology.com/p/journalology-151-the-midas-touch)).
### The Rise of AI in Mathematics
A notable instance of AI's impact on research was highlighted by mathematician Levent Alpöge, who announced a significant mathematical discovery via Twitter, utilizing advanced AI tools. This rapid dissemination of findings contrasts sharply with traditional publication timelines, raising questions about the future of scholarly communication and the role of AI in accelerating research outputs ([Scholarly Futures](https://scholarlyfutures.substack.com/p/frontier-models-transparency-and)).
## 2. Challenges in Traditional Publishing Models
### AI Startups and Publication Practices
A recent analysis revealed that many leading AI startups are not actively publishing their research, with over half of AI unicorns having never contributed to scientific literature. This trend raises concerns about transparency and the dissemination of knowledge within the scientific community, as these companies often prioritize speed and proprietary interests over traditional publication practices ([Journalology](https://newsletter.journalology.com/p/the-jist-july-8-to-july-27)).
### Ethical Concerns in AI Research
The ethical implications of AI in research were further underscored by a tragic incident involving a gene-editing trial that resulted in a child's death. The subsequent publication of research without addressing these ethical concerns highlights the potential pitfalls of prioritizing rapid publication over responsible scientific practices ([Journalology](https://newsletter.journalology.com/p/the-jist-july-8-to-july-27)).
## 3. New Collaborative Frameworks and Open Access
### Collaborative AI Training in Pharma
Pharmaceutical companies are increasingly collaborating to train AI tools, addressing the challenge of insufficient data for effective AI applications in drug development. This shift towards collaborative models reflects a broader trend in scholarly publishing, where open access and shared resources are becoming essential for advancing research ([Mixture](https://mixture.substack.com/p/behind-the-scenes-of-a-science-story)).
### Emergence of New Open Access Journals
In response to dissatisfaction with traditional publishing practices, editors from an economics journal are launching a new diamond open access journal under Stanford University Press. This initiative aims to provide a more affordable and accessible publishing option, reflecting a growing movement towards more equitable scholarly communication ([Journalology](https://newsletter.journalology.com/p/the-jist-july-8-to-july-27)).
## 4. The Future of Scholarly Communication
### The Role of AI in Shaping Research Landscapes
As AI continues to transform research methodologies, there is a growing concern about the accessibility of cutting-edge tools. Researchers at private AI firms often have exclusive access to advanced models, potentially widening the gap between those within these organizations and the broader academic community. This disparity raises critical questions about equity in research opportunities and the future of collaborative scientific inquiry ([Scholarly Futures](https://scholarlyfutures.substack.com/p/frontier-models-transparency-and)).
### Licensing and AI Integration
Emerging frameworks for AI licensing are becoming crucial as publishers seek to navigate the complexities of AI-generated content. Initiatives like the Microsoft Publisher Content Marketplace aim to facilitate responsible AI usage by establishing clear licensing terms, ensuring that content is both discoverable and ethically utilized ([Augmenting Scholarly Publishing](https://chiragjaypatel.substack.com/p/augmenting-scholarly-publishing-intelligent-215)).
## Conclusion
The landscape of scholarly publishing is rapidly evolving, driven by advancements in AI, shifting publication practices, and a growing emphasis on ethical considerations. As these trends continue to unfold, stakeholders in the academic community must navigate the challenges and opportunities presented by these changes to foster a more inclusive and transparent research environment.
_Generated from 5 items across 4 feeds._