Welcome to Tech Minute. Today we’re asking: How much energy does AI really use? It turns out nobody’s quite sure. In a recent WIRED deep dive, reporters reveal that companies guard the numbers on data-center power like trade secrets. We know a single ChatGPT query uses about 0.34 watt-hours of energy—“about what an oven would use in a little over one second, or a high-efficiency lightbulb would use in a couple of minutes,” as OpenAI CEO Sam Altman explained. But that’s just the tip of the iceberg. Training massive models can consume hundreds of megawatt-hours and require racks of specialized chips. Major cloud providers publish only broad totals, making it impossible for researchers to nail down AI’s true carbon footprint. Critics warn that with AI demand set to explode, the industry must open its books or risk unchecked emissions. Until then, the real cost of our digital assistant remains shrouded in mystery.
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Hello and welcome to the Continuous Delivery Podcast. Today we’re diving into Dave Farley’s take on a question every team grapples with: what’s the real difference between a coder, a developer, and a software engineer?

Farley starts by unpacking the coder. “A coder writes code,” he says, and that’s it. They take a specification, translate it into syntax, and move on. The developer steps up a notch: they not only write code, they own features, collaborate with teammates, and consider user needs along the way. As Dave puts it, “A developer builds a solution, not just lines of code.”

And then there’s the software engineer. Farley argues that engineers bring an end-to-end mindset. “They design systems with resilience, scalability, and maintenance in mind,” he explains. Engineers think in terms of boundaries, trade-offs, and long-term health. They establish standards, automate delivery pipelines, and take responsibility for operability in production.

So whether you identify as a coder, developer, or engineer, Farley reminds us that it’s all about mindset, ownership, and the scope of responsibility. That’s it for today’s episode—thanks for listening!
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Welcome to Linux Insights. Today we’re diving into how to use systemd for auto-mounting storage volumes on your workstation or server. As Jay LaCroix explains, “mounting volumes with systemd is very effective and is the recommended method going forward.”

First, identify your block device—say /dev/sdb1—and create a matching .mount unit in /etc/systemd/system. Name it after the mount path, for example, media-data.mount, and set the correct [Mount] options: What device, what directory, and your filesystem type. Then create a media-data.automount unit with [Automount], pointing to the same mount file, and set IdleTimeoutSeconds if you want it to unmount after a period of inactivity.

Enable both units with “systemctl enable media-data.automount” and start them. Your volume will only mount when you access the directory, speeding up boot and keeping things tidy.

For NFS shares, you can skip separate units by adding x-systemd.automount to your /etc/fstab entry. This tells systemd to handle the share the same way, mounting on demand.

That’s it—fast, reliable, and exactly why systemd is the modern path to managing your storage. Thanks for listening!
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Welcome to Wired’s one-minute podcast. Today we’re talking about a new breed of AI that, as the article puts it, “never stops learning.”

Modern large language models might write beautiful sonnets and elegant code, but they lack even a rudimentary ability to learn from experience. This new model flips that script. It’s built to update its own knowledge on the fly, drawing on fresh data streams and real-time feedback to refine its understanding.

Rather than freezing its world view at the end of training, it continuously adjusts its internal map. If you correct it, it registers that correction. If the world changes, it pulls in new facts. Lead researcher Dr. Mira Zhang tells Wired, “It’s like giving the model a never-ending fountain of knowledge.”

Of course, challenges remain—how to prevent bad actors from poisoning its updates, or how to ensure it doesn’t drift away from reliable facts. But this marks a big shift: AI that adapts and grows, not just repeats. That’s Wired in sixty seconds—thanks for listening.
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Hello and welcome to Tales from the Tar Heel Traveler. Today we head to North Carolina to meet a man who has been collecting baseball cards for decades. What started as a childhood hobby has grown into a treasure trove of sports history.

“I remember buying my first card for a penny,” he says, recalling that dog-eared 1950s card that sparked his passion. Over the years, he’s tracked down rare gems like a 1914 Babe Ruth rookie card and a pristine 1952 Topps Mickey Mantle, calling each one “a time capsule of America’s pastime.”

He travels swap meets and auctions across the state, driven by the thrill of the hunt. “Every card tells a story,” he explains, whether it’s a rookie debut or a championship moment frozen in ink. For him, this isn’t just paper—it’s a connection to legends and to memories of afternoons spent dreaming big.

That’s our stop on Tar Heel Traveler. Thanks for listening, and keep chasing your own slice of history.
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Welcome back to The Global Minute. Today, we’re zeroing in on a growing missile imbalance in the Middle East. According to a U.S. official and an analyst familiar with the numbers, “Iran has more offensive missiles than Israel has interceptors.” That alarming gap threatens to upend Israel’s missile defense strategy, which relies heavily on the Arrow 3 system. The Arrow 3 interceptors have been critical in countering long-range ballistic missile threats from Iran and Yemen, but today they “are now in short supply.”

With fewer Arrow 3 batteries available, Israel may face tougher choices if missile volleys increase. Options on the table include accelerating production of new interceptors, deepening cooperation with the U.S. for additional defense shipments, or investing in alternative systems like David’s Sling and Iron Dome for layered protection.

As tensions simmer across the region, this deficit underscores the urgent need for bolstered defenses—and sparks fresh debate over how best to deter future attacks. That’s your one-minute update. I’m [Host Name], thanks for listening.
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Hello and welcome to C++ Insights. Today we’re diving into Sandor Dargo’s article on variadic class template arguments. As he puts it, “Variadic templates don’t accept a fixed size of parameters, but anything from one to more,” and that flexible set is known as the parameter pack.

First, you can expand your pack in a constructor or member function. For example, a Storage class uses a std::tuple to hold elements of any types passed in. You might even need a simple deduction guide so you don’t have to list each type explicitly.

Next, you can expand at the inheritance list. Dargo shows a Visitor struct inheriting from multiple lambdas, using “using Ts::operator()…” to bring each callable into scope. Thanks to Class Template Argument Deduction, you can “inherit from lambdas without saving their generated types anywhere” and build a slick visitor in just a few lines.

And coming in C++26, you can even expand variadic friends: “friend Ts...;” makes it easy to grant access to any number of friend classes.

That’s a quick tour of variadic class template arguments—see you next time on C++ Insights!
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Today we’re talking about Haim’s new album, I Quit — their fourth record and a bold statement on love, freedom, and that chaotic space between “No, thanks” and “I do.” Danielle, Alana, and Este Haim wrote these songs during a once-in-a-century breakup spike: “We ended our relationships almost at the same time,” Este told The New Yorker. The result is an album about shedding control and finding relief.

On the tense single “Now I’m in It,” Danielle sings, “We cannot be friends / Cannot pretend / That it makes sense,” and on “Gone” she preaches liberation: “I’ll do whatever I want / I’ll see who I wanna see / I’ll fuck off whenever I want.” My favorite, “Everybody’s Trying to Figure Me Out,” pulses with jittery snare and a haunting verse: “Smoked out, bought a pack of Lights…built a jacked-up time machine to prove I was right.” By the end, she reassures herself — and us — “You think you’re gonna die / But you’re not gonna die.” I Quit reminds us that heartache is surmountable, and sometimes rocking out is the best therapy.
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Hello and welcome to Hack Hacks, the podcast where we celebrate the cleverest projects in the maker world. Today, we’re talking about how one engineer actually weighed an airplane as it flew overhead. That’s right—[AlphaPhoenix] “weighed an airplane,” and even a paper airplane, using a do-it-yourself scale made of foamcore and four load cells hooked up to an Arduino and a laptop for visualization.

After a quick test with a toy car, our builder sent a paper plane gliding over the scale. The result? A clear demonstration that “any weight from a flying object is eventually transferred to the ground via the air.” But the fun didn’t stop there. Next, they built a smaller device—a pressure sensor in an airtight box, basically a fancy barometer—and stationed it beneath real airliners at a local airport for three days.

They “took measurements, made assumptions, and arrived at figures,” even though those results were “off by more than one order of magnitude.” Still, it’s an unbelievably cool experiment that shows just how creative hacking can be. Thanks for listening, and keep on making!
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Hello and welcome to Rapid Response. Today: Reid Hoffman on Musk vs. Trump and the real AI threat to jobs. Amid global unrest and surging AI, Hoffman says “we live in chaotic times” as he dissects Trump’s spat with Elon Musk, warning that political engagements haven’t paid off in business for Musk and have even irritated Tesla buyers.

On geopolitics, Hoffman believes ending the post-WWII world order risks “increasing conflict and chaos everywhere.” Yet he values negotiation over violence, saying “words are always better than physical violence.”

Turning to AI and employment, he critiques predictions of a “white-collar bloodbath,” arguing “anyone who claims that they know exactly what the impact is is either self-deluding or other-deluding.” Instead, he foresees a “tsunami of transformation,” with humans and AI working together, not wholesale job destruction.
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