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The article examines the growing threat of weather data manipulation, exacerbated by prediction markets and AI forecasting, and highlights a real-world incident where a Paris weather station was tampered with to influence betting outcomes.
This paper identifies a failure mode in agentic LLM tools like Claude Code, where session compaction summaries misinterpret partial terminal output from timed-out commands as confirmed results, propagating false positives across sessions and model versions without re-verification.
The article argues that SQLite's defaults for foreign key constraints and type enforcement are problematic and suggests adopting Rust-style editions to allow users to opt into safer defaults.
Bab is a family of hashing functions designed for peer-to-peer networks, enabling verifiable partial data. It was funded by the NLnet Foundation and the European Commission's Next Generation Internet programme.
Discusses how AI systems often trust sensor inputs without validation, using an example of a logistics company where spoofed temperature sensor data led to cargo damage, and questions whether AI can detect such spoofing.
This article argues that the next generation of AI depends on a healthy human ecosystem, emphasizing the need to prioritize human health, data integrity, and environmental stability as engineering requirements.
A software tool designed to detect data fabrication has uncovered copy-paste errors in scientific datasets across open-access repositories, including a highly-cited Parkinson's Disease study in Cell (2016) that has been publicly available for over 8 years without detection.