iPhone 17e comes with iOS 26, delivering a beautiful new design, powerful Apple Intelligence capabilities, and meaningful improvements to the apps users rely on every day.7 The new design with Liquid Glass makes apps and system experiences more expressive and delightful, bringing greater focus to content while keeping iOS instantly familiar and introducing even more ways to personalize iPhone. Apple Intelligence allows users to communicate across languages with Live Translation in Messages, FaceTime, Phone, and with AirPods.8 Visual intelligence now extends to a user’s iPhone screen, letting them search, ask questions, and take action on the content they’re viewing.9 To help users eliminate distractions, Call Screening can screen calls from unknown numbers and automatically ask the reason for calling, while Hold Assist can hold on the line until a live agent is available.10 In Messages, users can now choose to screen messages from unknown senders by filtering them to a dedicated folder so they don’t clutter up the conversation list.
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both of these approaches use NFAs under the hood, which means O(m * n) matching. our approach is fundamentally different: we encode lookaround information directly in the automaton via derivatives, which gives us O(n) matching with a small constant. the trade-off is that we restrict lookarounds to a normalized form (?<=R1)R2(?=R3) where R1/R2/R3 themselves don’t contain lookarounds. the oracle-based approaches support more general nesting, but pay for it in the matching loop. one open question i have is how they handle memory for the oracle table - if you read a gigabyte of text, do you keep a gigabyte-sized table in memory for each lookaround in the pattern?
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