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Layered voice notes that paint themselves Discussion | Link

2026-06-12 02:08 👁 4 查看原文 →
The Verge AI

Amazon’s data centers used 2.5 billion gallons of water last year

Just after Seattle enacted a one-year data center moratorium that some of Amazon's own employees pushed for, Amazon shared how much water its data centers use, reportedly for the first time. With concerns about water consumption and energy use a focus of new AI data center construction debates, Amazon says its global data center operations […]

Stevie Bonifield 2026-06-12 01:26 👁 8 查看原文 →
The Verge AI

The bill that would let Jimmy Kimmel sue Brendan Carr is here

Under a new bipartisan bill, Americans could sue for damages if a government official illegally tries to coerce a social media, AI, or broadcasting company to remove their post - regardless of whether the platform actually does it. Senate Commerce Committee Chair Ted Cruz (R-TX) and Sen. Ron Wyden (D-OR) introduced the JAWBONE Act on […]

Lauren Feiner 2026-06-12 01:23 👁 10 查看原文 →
The Verge AI

Here are the price-matching policies for Best Buy, GameStop, and others

Nothing is more frustrating than buying a new pair of headphones, an OLED TV, or a laptop just to find out that you could have gotten it for cheaper somewhere else just a few days or weeks later. That’s why, in order to keep customers happy and prevent them from shopping elsewhere, some retailers offer […]

Sheena Vasani 2026-06-12 01:14 👁 10 查看原文 →
HackerNews

Ask HN: Agents get dumber before release of new model version?

I've noticed an effect with openai where my codex agents seem to perform worse in the week(s) leading up to a new release. I'm wondering if the vendors tweak effort params at all to free up hardware to host the new version. It's a double win as the new model will look night and day better to their regular users when the new model is released presumably with effort back at normal levels. Is this a known phenomenon? Are any folks trying to measure any of this objectively?

sporkland 2026-06-12 00:27 👁 3 查看原文 →
The Verge AI

A warrantless wiretap law is about to expire — but surveillance networks aren’t actually ‘going dark’

Congress has failed to pass a three-week extension of Section 702 of the Foreign Intelligence Surveillance Act (FISA), with the House voting 218-198 against reauthorizing the controversial warrantless wiretapping authority through July 2nd. After a short-term extension earlier this year, the spying program now appears set to lapse for at least a week. This is […]

Gaby Del Valle 2026-06-12 00:03 👁 11 查看原文 →
Dev.to

Building Video Heatmap Analytics with HyperLogLog in Postgres

The problem: counting unique viewers per second is a row explosion A viewer scrubs to 4:12 of a 9-minute trending clip, watches for 40 seconds, jumps back to the intro, then bounces. Multiply that by the few hundred thousand sessions a day that hit a mid-size aggregator and you get the question every product person eventually asks: which parts of this video do people actually watch, and how many distinct people watched each part? The naive answer is a watch_events table: one row per (user, video, second) . It works until it doesn't. A 9-minute video is 540 seconds. One viewer who watches the whole thing generates 540 rows. A million viewers across our catalog generate hundreds of millions of rows per day , and the only query anyone runs against them is COUNT(DISTINCT user_id) GROUP BY second . That COUNT(DISTINCT) is a sort-or-hash over the entire partition every single time someone opens the analytics tab. At TopVideoHub we aggregate trending video across Asia-Pacific, so a single popular clip can spike from zero to half a million sessions in an afternoon when it lands in the JP and KR feeds simultaneously. We did not want a fact table that grew by hundreds of millions of rows a day to answer a question whose answer is approximately fine. "Roughly 41,000 unique viewers saw the hook at 0:08" is just as actionable as "41,287". That tolerance for approximation is exactly what HyperLogLog is built for, and Postgres has a battle-tested extension for it. This post is the design we landed on: fixed-size HLL sketches, one per (video, time_bucket) , that you can merge, slice, and union across regions in milliseconds. The main app is PHP 8.4 on LiteSpeed behind Cloudflare, with our search layer on SQLite FTS5; the analytics store is a separate Postgres instance, and HLL is what made that store affordable. Why HyperLogLog instead of COUNT(DISTINCT) HyperLogLog estimates the cardinality of a set using a fixed amount of memory regardless of how many elements you throw at it. Th

ahmet gedik 2026-06-12 00:00 👁 11 查看原文 →