Tag
Cartopolis is an open-source interactive 3D world built from public data, enabling users to explore and socialize in a virtual real-world environment.
A study interviewed four AI models on 24 subjects, recording 1,452 positions to archive their explicit views when pushed for consistency.
A publicly available AI monitor tool that aggregates data on AI models, developers, countries, usage, performance, incidents, and the EU AI Act into an interactive environment for exploration and comparison.
Public data shows that leading tech entrepreneurs are becoming active investors in startups, with Guillermo Rauch having the most investments at 128, followed by Immad Akhund and others.
An autonomous research agent built with Atomic Mail Agentic emails site maintainers to correct public data, highlighting the role of sender credibility in unsolicited communications.
The article discusses the ethical and practical distinctions between AI personalization in cold emails using publicly sourced data versus behavioral data, noting how these differences impact recipient reactions and complaint rates.
Bribes.fyi is a web tool that allows users to compare bribe statistics across different departments, providing insights into corruption data with side-by-side comparisons.
A data journalism piece analyzes 50,000 boat names from NOAA vessel traffic data, highlighting puns, jokes, and pop culture references, with an interactive searchable visualization.
Conservationist Raffael Hickisch used exe.dev's AI agent Shelley to build Five Megapixel Conservation, an app that aggregates satellite and public data into a global map showing conservation efforts, enabling real-time monitoring of protected areas.
A new paper builds and releases a corpus of 2.2 million city and county laws from across the United States, making publicly accessible legal texts that were technically public but hard to find.
This paper introduces Asymmetric Langevin Unlearning (ALU), a framework that leverages public data to improve the privacy-utility trade-off in machine unlearning. It demonstrates that ALU reduces unlearning costs and enables mass unlearning while maintaining high model utility.