A study evaluated how polygenic risk score training strategies transfer from European GWAS data to Japanese samples across eight clinical traits. The largest dataset transferability tests compared training when the target-population (Biobank Japan) sample size reached 15,000 and found that European-only pooling helped only below that cutoff. Model performance then crossed over and increasingly favored target-population-specific training as Biobank Japan sample sizes grew, with conserved traits retaining benefits longer and population-specific traits favoring alternative methods like cross-population meta-analysis.
PhiloLabs ran Claude Fable 5.1 coding agents to recreate a 3D Union Square in the browser using real geographic data and reference images. The full run used about 8 million tokens and cost about $33 in API calls. Reviewers used Playwright screenshot checks and nine reports to create a punch list for the agents, showing that conventional tests would miss visual and proportion issues.
OpenAI started publicly previewing how it evaluated Astra before release, tying the process to the model’s capabilities. The preview focuses on safeguards added after Astra’s capabilities were assessed, specifically to control what it can do. As a result, Astra’s release is accompanied by stronger, capability-based safety measures.
A Ph.D. graduate in South Africa refused to train an AI system that would design assessments, teach undergraduates, and mark essays. The recruiter offered 600 rand (about $37) per hour, compared with a national minimum wage of 30.23 rand (about $2) per hour, amid 47.4% youth unemployment in Q2 2026. As a result, she stopped pursuing the role after an AI interview and continues searching for an academic job rather than AI work.
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