Пользователи мессенджера "Макс" после его удаления из маркета AppStore от Apple могут начать переходить на другие устройства, сказал журналистам на ПМЭФ президент "Ростелекома" Михаил Осеевский.

"Поскольку iPhone перестанут с "Максом" работать, конечно, трафик уменьшится. Объективно. Дальше либо люди будут переходить на телефоны на Android, или на других операционных системах, например, на [российскую] "Аврору" [переходить]", - сказал топ-менеджер, отвечая на вопрос ТАСС.

Он подчеркнул, что российский национальный мессенджер уже "устойчиво занял свое место". Ссылаясь на данные входящего в "Ростелеком" оператора Т2, Осеевский заметил, что, по данным на прошлую неделю, только в сети T2 было 24,5 млн пользователей "Макса".

Тихий океан знаешь? Армяне успокоили

We presented the first comprehensive study on the performance impact of exploiting undefined behavior (UB) in C and C++ programs. First, we cataloged 18 individual UB aspects exploited by LLVM, a compiler that is widely known for its extensive use of UB. We then implemented flags in the compiler that disable exploitation of each UB aspect by strengthening the semantics (e.g., define division by zero as a well-defined trap instead of UB).
The results show that, in the cases we evaluated, the performance gains from exploiting UB are minimal [выделение моё]. Furthermore, in the cases where performance regresses, it can often be recovered by either small to moderate changes to the compiler or by using link-time optimizations

Using a large-scale controlled resume correspondence experiment, we find that LLMs consistently prefer resumes generated by themselves over those written by humans or produced by alternative models, even when content quality is controlled. The bias against human-written resumes is particularly substantial, with self-preference bias ranging from 67% to 82% across major commercial and open-source models. To assess labor market impact, we simulate realistic hiring pipelines across 24 occupations. These simulations show that candidates using the same LLM as the evaluator are 23% to 60% more likely to be shortlisted than equally qualified applicants submitting human-written resumes, with the largest disadvantages observed in business-related fields such as sales and accounting. We further demonstrate that this bias can be reduced by more than 50% through simple interventions targeting LLMs' self-recognition capabilities.