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?

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

In December 2025, Anthropic acquired Bun , the JavaScript runtime written in Zig. In April 2026, the Bun team announced a 4× compile-time improvement on their fork of the Zig compiler — "parallel semantic analysis and multiple codegen units to the llvm backend" , in their phrasing. They also announced they would not be upstreaming the work, "as Zig has a strict ban on LLM-authored contributions." The framing landed badly with Zig observers, for two reasons. The first was that the framing made Zig's contribution policy the obstacle. The second, pointed out shortly afterwards by a Zig core contributor in the Ziggit thread, was that the patch had separate engineering reasons it would not have been merged regardless: "Parallel semantic analysis has been an explicitly planned feature of the Zig compiler for a long time" , with "implications not only for the compiler implementation, but for the Zig language itself" . The AI-ban explanation was, on a closer read, a tidy way of declining to litigate the engineering disagreement in public. Both readings are useful. They are also both downstream of the actual rationale, which is one of the most carefully argued OSS-governance documents to appear in 2026. What the policy actually says The relevant clauses, in the Zig code of conduct under the section heading Strict No LLM / No AI Policy , are three: No LLMs for issues. No LLMs for pull requests. No LLMs for comments on the bug tracker, including translation. English is encouraged, but not required. You are welcome to post in your native language and rely on others to have their own translation tools of choice to interpret your words. The translation clause is the surprising one. It is also the one that disambiguates the policy from a code-quality rule. A blanket ban on LLM-mediated communication, including translation, is not a heuristic about whether agentic tools produce good code. It is a stance about what the project's communication channels are for . Contributor poker Lor

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 .