Evidence-linked signals and analysis about AI capabilities, work, skills systems, labour markets and governance — written for the decision that follows.
GitHub’s compact classifier reports higher precision, recall and alert acceptance than its prior LLM pipeline. Security teams should reproduce the decision boundary with their own false negatives before changing controls.
A 291-page enterprise study maps 500-plus use cases and 272 consultation responses across four sectors. Its most transferable idea is a pilot with a baseline and an explicit production decision.
StudentSim models how 60 learners answer and revise across chess, English writing and mathematics. Its proper place is before a human study—not in place of one.
A new preprint turns vague promises such as “every action is logged” into claim contracts: policy, scope, records, writers and a decision rule fixed before evaluation.
A census found public safety-test results for 31 of 857 Chinese AI-model releases. Procurement teams should treat the denominator as an evidence-availability measure, not a safety score.
The UK ICO is gathering evidence on agentic AI until 20 November. Deployers should map every data-reading and data-changing action now instead of waiting for final guidance.
Muse Wonder adds a new frontier-model option with published pricing and benchmark claims. Test one bounded workflow for reliability, permissions and cost before changing a default.
A cryptomining campaign used public services, rotating infrastructure and a poem-hosted address trick. The workforce decision is simple: assign ownership and patch evidence for every exposed AI endpoint.
A controlled study found that LLM evaluators favoured résumés generated by the same model. Hiring teams need a model-origin invariance test before automated shortlisting affects candidates.
