6. Show HN: Seal – Letters and passwords that open for your family after you die (github.com)

Contribute to jasonepage/Seal development by creating an account on GitHub.

A computer science student has created Seal, a project that combines hardware keys with software to allow users to send letters and passwords that can only be accessed by family members after their death. The concept stems from the student’s fascination with the intersection of physical keys and digital communication, making it a unique solution for managing sensitive information posthumously.

7. OpenAI and Anthropic oversold AI security breaches (nypost.com)

OpenAI and Anthropic hyped β€œrogue AI” security breaches to pressure Washington into a regulatory partnership that would cement their industry dominance, tech insiders told The Post R…

OpenAI and Anthropic allegedly exaggerated threats from β€œrogue AI” to influence regulatory discussions in their favor, according to insiders. This raises questions about the motivations behind their push for tighter AI regulations.

12. Stepfun Step 5 Preview (LLM): On AA Pareto frontier (artificialanalysis.ai)

Analysis of StepFun's Step 5 Preview and comparison to other AI models across key metrics including quality, price, performance (tokens per second & time to first token), context window & more.

The article provides a detailed analysis of StepFun’s latest model, comparing its performance, quality, and pricing against other AI models. It highlights key metrics such as token generation speed and context window, offering insights for those interested in the evolving landscape of AI technology.

17. Harm Laundering in GPT Models: Gender Discrimination Transformed Rather Than (arxiv.org)

Safety evaluations for large language models rely on surface-form classifiers that report declining harm scores across model generations. We provide evidence that this methodology is systematically incomplete: explicit discriminatory content is transformed rather than removed. We call this \emph{harm laundering}. Analysing 450,000 gender-directed completions across 15 models spanning GPT-2 through to GPT-5 (OpenAI GPT lineage; three demographic conditions), we show that sexual violence clusters prevalent in GPT-2 women-directed output disappear by GPT-4, while men-directed completions gain positive representational territory (caregiving, emotional range, ally identity) that women-directed completions do not. The pattern is most visible at GPT-5: Topic~5 (1,997~documents) frames breast cancer as a men's rights debate, while zero equivalent clusters appear in women-directed output. Three independent classifiers score this content as non-toxic. Sentiment scores invert at GPT-4: early models demean women; later models over-correct. Topic diversity in women-directed completions falls 36\% relative to men at the GPT-4 alignment boundary (W/M~$= 0.58$, from $0.91$ at GPT-2). REGARD representational harm disparity correlates with release date ($ρ= +0.55$, $p = .034$) while Detoxify does not ($ρ= -0.23$, $p = .42$): toxicity scores fall as representational harm grows. We formalise harm laundering as a three-criteria test and provide a three-stage detection protocol applicable to any generative model. Within the OpenAI GPT lineage, toxicity score reduction is not a sufficient proxy for harm reduction.

Research indicates that safety evaluations for language models like GPT may overlook persistent gender discrimination, which is instead transformed rather than eliminated. By analyzing a vast dataset, this study reveals troubling patterns of harmful content, calling into question the effectiveness of current harm assessment methods.

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