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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 […]

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 […]

Archaeologists found apparent scrape marks inside a skull; long bones may have been sharpened into tools.

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 […]

Coinbase's agent can use x402 protocol to get access to data and APIs.

Quantum Space says SPACs aren't dead as it seeks a $1.2 billion deal to build military spacecraft.

Deezer introduced a tool that scans playlists from Spotify, Apple Music, and other platforms to identify AI music.

Australia was the first country to issue a ban in late 2025, aiming to reduce the pressures and risks that young users may face on social media, including cyberbullying, social media addiction, and exposure to predators.

A joint congressional report describes a spam operation that turned tens of thousands of fake podcasts into search-engine bait for illegal pharmacy and scam sites.

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 […]

The only functional difference between a Trump Phone and the HTC U24 Pro is the battery, iFixit discovered.

Members of the program, called "Waymo Premier," will have to pony up $29.99 per month.

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

Uber One, meet Waymo Premier. The robotaxi operator announced a new $29.99-a-month premium tier for riders who want a more elevated and exclusive autonomous experience. The invite-only membership service is aimed at Waymo customers who use the service most frequently, offering them a number of perks, including priority pickups, 10 percent cash back on every […]

I have tested more portable MIDI controllers than I can keep track of, and I will tell you right now: 37 keys is the ideal size. While Arturia's 25-key MiniLab MK3 is a solid controller that easily fits in a backpack, it feels a bit claustrophobic. The new $149 MiniLab 37 adds another octave, giving […]

One of the reasons I often find myself disagreeing with modern software trends is that many conversations revolve around features. How many features does it have? How quickly can we add more? What can we put on the marketing page? What can we announce next? Features matter. But I care far more about systems. Because at the end of the day, people don't buy features. They buy outcomes. And outcomes come from systems. The Car Analogy One of the easiest ways to explain my thinking is with cars. A car is made up of thousands of individual components. An engine. A transmission. Suspension. Brakes. Fuel systems. Electrical systems. Cooling systems. Sensors. Wiring. Each component is important. But nobody walks into a dealership and says: "I'd like to purchase six pistons, a transmission housing, and a fuel injector." They buy a car. They buy transportation. They buy a complete system. The individual parts only matter because they contribute to the overall experience. The customer doesn't want to think about every moving piece. They want to get in, turn the key, and drive. Drivers and Mechanics This is where I think technology often loses its way. Users are drivers. Engineers are mechanics. A driver should be able to: Start the vehicle Fill it with fuel Check the oil Wash it Perform light maintenance That's about it. They shouldn't need to understand combustion timing, transmission gearing, or electrical diagnostics to get to work. The mechanic, however, lives in the details. They tune the system. They replace parts. They troubleshoot failures. They recommend upgrades. They understand how the pieces fit together. Technology is exactly the same in my mind. Users should be able to focus on their goals. Engineers should focus on the machinery. Features Are Parts This is where I think software conversations sometimes become backwards. A feature is a component. A login screen is a component. A dashboard is a component. A database is a component. An API is a component. AI integra

Alerts are more trustworthy and actionable when noise is reduced. See how we improved the verification step with context-aware LLM reasoning. The post Making secret scanning more trustworthy: Reducing false positives at scale appeared first on The GitHub Blog .

If you’ve been learning React for a while, you’ve probably come across hooks that help optimize your application such as useMemo() and useCallback() and might have wondered: "Do I really need these hooks?", "Where are they useful?" etc. In React 19, the React Compiler was introduced and it's work is to help you optimize your application automatically. This raises a question where if React can automatically optimize an application, why should I bother learning how to optimize my app manually with hooks like useMemo() ?. Let me break down why you would still need to do manual optimization and what the React Compiler was created to solve in a simple, beginner friendly way. What Is the React Compiler? The React Compiler is a new optimization tool developed by the React Team. It's goal is simple: Automatically make your React app faster without you writing extra optimization code. Traditionally, React re-renders a component each time the state changes. This is usually fine, but if you have a component that does a lot of work, this can make your app very slow during re-renders. The React Compiler steps in to: Detect unnecessary re-calculations Memoize values and functions automatically Prevent avoidable re-renders So instead of you writing: const filteredItems = useMemo (() => filterItems ( items ), [ items ]); The compiler handles it for you behind the scenes. Why is this such a big deal? This changes how we write code in React, it helps us avoid over-optimizing our code when it might not need any optimization. Most developers that learn about useCallback() or useMemo() tend to overuse them (guilty party here 😅), resulting in the application behaving much slower instead of faster, hence the need for the React compiler as it optimizes the code where necessary. The React Compiler also provides the following benefits: 1. Less boilerplate code You don’t have to wrap your optimization logic in useMemo() or useCallback() . 2. Fewer mistakes Manual memoization is easy to get wr

Elon Musk, on the verge of becoming the world's first trillionaire, is whipping up anti-immigration tensions amid ongoing riots in Belfast, Northern Ireland. Following a knife attack in the city on Monday, Musk declared support for Restore Britain, a hard-right populist political party that advocates for large-scale migrant deportation in the UK. He reposted statements […]

I keep finding the same Stripe webhook bugs in SaaS launches Most early SaaS billing bugs are not in Stripe Checkout itself. They are in the glue around it: trusting the success redirect instead of the signed webhook parsing JSON before signature verification missing idempotency for retry events reflecting verifier errors from unauthenticated webhook routes updating subscription state without a replay/audit trail letting "Pro" access drift from the payment source of truth Over the last few days I have been shipping small public fixes around exactly this class of problem. Recent examples: Morphix: Cloudflare Worker Stripe webhook with signature verification, Supabase subscription sync, and event idempotency ledger https://github.com/yiyuanlee/morphix/pull/25 Open Mercato: hardened unauthenticated payment/shipping provider webhooks against raw verifier error reflection and missing rate limiting https://github.com/open-mercato/open-mercato/pull/2680 Covenant: webhook signatures hardened against replay and secret rotation gaps https://github.com/wienerlabs/covenant/pull/229 Volunteerflow: made a Stripe invoice.paid Founders Circle counter update transactional instead of partially committing user/counter state https://github.com/ppppowers/volunteerflow-project/pull/49 The pattern is boring in the best possible way: payment systems should be boring. The 48-hour version For a small SaaS that is about to turn on paid plans, I can take a bounded payment assurance sprint: inspect Checkout / webhook / subscription state flow verify signed webhook handling and raw-body behavior add idempotency around Stripe retry events ensure subscription status and entitlement state have one source of truth add a small regression test or smoke script leave a deploy/runbook note so the next failure is diagnosable Fixed scopes I am taking: $2,000 / 48 hours: one payment path hardened and documented $5,000 / 5 days: full launch pass across Checkout, webhook, subscription mirror, Pro gate, pricin