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Google Is Suing Chinese Scammers Who Are Using Gemini

Not sure this will have any effect, but I support the effort: According to Google’s legal filing, Outsider Enterprise operates through Telegram. The group offers phishing-as-a-service to individuals who may not be technically savvy enough to set up fraudulent websites and text campaigns on their own. In its Telegram channels, Outsider Enterprise reportedly provided instructions on how to use Google’s Gemini AI to create websites that imitate those of Google, YouTube, and government agencies such as New York’s E-ZPass. The group offered nearly 300 scam templates...

2026-07-07 原文 →
AI 资讯

How to track Weibo hot-search velocity with Python in 2026 — the trending-delta problem and how to handle it

If you scrape Weibo's hot-search board you get a snapshot: ~50 trending topics, ranked, right now. That's table stakes — and on its own it's almost useless as a signal. The value isn't what is trending; it's what's moving : which topic just jumped 30 places in 20 minutes, which is decaying, which is brand-new this hour. That's velocity , and velocity is where the signal lives — for brand-crisis teams, consumer-trend desks, and anyone modelling attention in China. The catch: a single scrape can't tell you velocity. You have to diff the board against its own past, reliably, run after run. That's a stateful pipeline, and it has a few non-obvious gotchas. Here's the shape of the problem and how to handle it. Why a snapshot isn't enough Rank-right-now tells you nothing about trajectory. "#7" could be a topic on its way to #1 or one fading out of the top 50 — same row, opposite meaning. To act on a trend you need the derivative : direction, speed, and how long it's been climbing. None of that is in a single pull. The trending-delta problem Three things make "just diff the board" harder than it looks: Key by identity, not position. You can't track a topic by its rank — rank is the thing that changes. Key by the topic itself (its text/keyword) or your deltas are nonsense. State has to survive between runs. A scheduled scrape is stateless by default — each run starts cold. To compute "this rose 12 places since 30 minutes ago," you must persist the previous board and reload it next run, keyed so independent schedules don't overwrite each other. The board churns. Topics appear, peak, and fall off. You want each tagged new / rising / falling / steady / dropped , plus how long it's been on the board and its running peak — none of which exist in the raw snapshot. How to handle it (the pattern) current = pull_board () # [{topic, rank, heat}, ...] previous = load_state ( key ) # durable store that persists across runs for t in current : prev = previous . get ( t . topic ) # match o

2026-06-09 原文 →