Eagle - Organize design files has never been easier

A better way to collect, search and organize your design files in a logical way and all in one place.

DeepSWE

DeepSWE measures frontier coding agents on original, long-horizon software engineering tasks.

Datacurve | The data engine for frontier AI

Custom data for long-horizon reasoning, software engineering, and data science.

Haters

WebHaptics – Haptic feedback for the mobile web.

Haptic feedback for the mobile web.

Creem Pricing: 3.9% + $0.40 | No Monthly Fees

One rate. No monthly fees. Taxes, compliance, and fraud protection included. Start selling globally in minutes.

Best Local-First Mac Apps Directory | OwnYourMac

Discover the best local-first Mac apps. One-time payments or free. No subscriptions.

Into the feed of Currents, an open Pinterest alternative built on AT Protocol - Matteo Marjanovic

In this article I describe how I built the first version of Currents' feed, an open Pinterest alternative on AT Protocol, and how its personalization slider works.

On atmospheric generatives… - Scraps!

How can we build moats on an open network? How can we make money when all the data's free? Here, I sketch some ideas for new generatives — contextualization, fluidity, aggregation, and relationality — for value creation in the atmosphere.

Recommender systems and their ethical challenges

This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system.

A Literature Review of Ethical Considerations in Recommender Systems for User-Generated Content in Human-Computer Interaction

The design of user-generated content (UGC) platforms poses challenges in comprehensively addressing the ethical dimensions of recommendation algorithms and applying human-centered methods for their evaluation. This article presents a literature review of 97 studies on UGC algorithms (UGCAlgos) that incorporate human factors and user experience considerations to investigate the ethical issues explored in human-computer interaction (HCI) research. Our review identifies key themes in the ethical considerations surrounding UGCAlgos and the user modeling methods employed. We examine how common ethical concerns in recommender systems, such as content appropriateness, privacy, user engagement, transparency, fairness, and diversity, are studied and contextualized within UGC platforms. Furthermore, we summarize how these concerns are addressed through user modeling approaches, including data characterization, user context, user outsmarting, interface design, user beliefs, and community and societal impacts.