Tijdens een boswandeling komt een vader met een dochtertje van drie à vier jaar oud en een hondje mij tegemoet.

Je krijgt een paar cijfers cadeau, maar het grid van 9x9 moet foutloos ingevuld worden.

Een Precies Vier bestaat uit 16 woorden, begrippen of namen, die moeten worden verdeeld in precies vier groepen van vier. Er is telkens maar één oplossing mogelijk. Welke woorden vormen een connectie?

Als je wel zin hebt om te sudokuen, maar het liever bij een gridje van 5x5 houdt.

Een kruiswoordpuzzel, maar dan heel klein (en snel).

German textile firm ZEGO has filed for insolvency and is blaming a March cyberattack that shut down production for nearly six weeks. "ZEGO's filing adds another name to the short but growing list of companies that say a digital break-in was commercially fatal to their business," reports The Register. From the report: In a notice to customers and suppliers, the organization said it had exhausted every available option before seeking insolvency protection. Managing director Johannes Zenglein described the filing as "one of the most difficult steps in our company's 37-year history." "The cyberattack of March 29, 2026, however, impacted our company to an extent that we could not fully compensate for despite our best efforts," Zenglein wrote. "The consequences resulted in a production outage of nearly six weeks and significant financial strain. These effects ultimately impacted our financial situation so severely that filing for insolvency became necessary."

ZEGO did not disclose what kind of attack it suffered, whether ransomware was involved, who was behind it, or whether customer or employee data was compromised. What it has made clear is that the operational disruption alone was enough to push the business beyond the point of recovery. ZEGO said insolvency proceedings have now been initiated, but insisted the filing does not necessarily spell the end of the business. It said it plans to keep production running while administrators attempt to restructure the business, preserve jobs, and keep customers and suppliers on board.

Read more of this story at Slashdot.

ntomlin124 has added a photo to the pool:

So bright contrasted against a dull background.

iain.davidson100 has added a photo to the pool:

One of my favourite poems has the line "The japonica glistens like coral. And today we have naming of parts."

iain.davidson100 has added a photo to the pool:

iain.davidson100 has added a photo to the pool:

It looks a bit surreal, but zoom in to see that it is quite a simple collection. And it must be made by a human, and I would guess that it is a product of the school holidays.

“A star located about 1,300 light-years from Earth shows signs of having devoured one of its planets — and is now gearing up for a second helping…” Scientists found signs of the meal by looking for “cosmic cookie crumbs”.

Meta seems to be having a bit of an identity crisis. On Monday, the social networking singularity said it would spend $50 billion to expand its Hyperion datacenter project in Richland Parish, Louisiana, from 2.2 to 5 gigawatts. The news comes less than a week after a report broke claiming that Meta was actively exploring options to offload its excess compute capacity to other AI labs. So, which is it, Zuck? Did you invest too much or too little in AI? The easy answer is that Meta overcommitted. Inspired by the early success of Llama, it made a huge bet on the AI gold rush. Offloading spare compute to the highest bidder is just a hedge in case its Superintelligence team turns out to be another pipe dream, like the Reality Labs Metaverse that utterly failed to spark enthusiasm for immersive environments accessible through Meta's Quest cybergoggles. The more pragmatic read is that Zuckerberg has woken up to the fact he’ll never be as cool as OpenAI boss Altman or Anthropic's Amodei, and renting out spare compute is just the natural progression for any sufficiently large hyperscaler. Dawn of the Meta cloud? Meta's business model is closer to Google's than those operated by OpenAI and Anthropic. Both Meta and Google offer various services which generate revenues by connecting users with advertisers. For Google it’s a search and entertainment empire. For Meta it's enabling an endless feed of content generated by friends, family, influencers, and yes, bots. Both are immensely profitable, earning $132.2 billion and $60.5 billion in profits last year, respectively. That's profit, not revenue. But both are now plowing over $100 billion a year into AI infrastructure to power large language and image and video generation models. As we learned from Meta’s recent earnings calls, the most commercially potent of those models get the right ads in front of the right eyeballs. The open secret is Meta was already one of the most successful AI companies long before ChatGPT debuted. Except, it's not large language models (LLMs) that make Meta money, at least not in the conventional sense. Instead, Meta’s most profitable AI models are the recommender systems that mine profiles for context and use it to infer your needs. Meta's devs evolved those models considerably over the past few years, and their architectures now look a lot more like an LLM than the now-pedestrian neural networks on which Zuckerberg built his empire. Google is in a similar situation. It’s investing heavily in AI to feed its fast-growing and profitable cloud business, even as advertising still pays most of the bills. But unlike Google, Meta hasn’t yet made the leap from hyperscaler to cloud provider. Amazon, Google, Microsoft, even Oracle got there eventually, and it seems AI may be the catalyst that turns Meta into a cloud, too. Recent reports suggest that Zuckerberg is warming to the idea. “I think that’s certainly a thing that we could do and that I think would make sense to consider,” he said in an interview with Bloomberg last week. “As a backstop, even if for whatever reason we don’t need all the compute ourselves or for any number of reasons, there’s a very large amount of demand that I think you could sell it long-term like AWS or Azure or Google Compute.” But while the demand may be there, Zuckerberg emphasized the compute capacity is not readily available. But as Ben Thompson of Stratechery put it, cashing in on this compute may be more than a backup plan. In a post channeling an imaginary Zuckerberg, Thompson suggested that becoming a neocloud would force Meta to stop chasing pipe dreams and pet projects. His logic is that if Meta can't make money with infrastructure it buys for AI ventures, the social networking giant can lease the orphaned hardware to the highest bidder. The takeaway for investors — should Meta follow its fellow hyperscalers-turned-cloud-providers down this road — is that the profitability of its hardware investments would no longer be tied to its ability to commercialize them. Seizing the means of production If history tells us anything, scale matters. Building a cloud like Amazon Web Services (AWS) is next to impossible unless you've already figured out how to profit from those same resources. Meta's scale puts it in a position to acquire compute in volumes impossible for smaller players. Its ability to capitalize on infrastructure demand relies entirely on having something others want but can’t get anywhere else. For what it’s worth, Zuckerberg wouldn’t be the first to come to this conclusion. Earlier this year Musk-owned xAI surprised many when it announced plans to rent out its Colossus supercluster in Memphis to rival model dev Anthropic. The calculus here is the same. Making a profit off LLMs, like Grok, isn't easy — just ask OpenAI — but selling the means of AI production to those that haven’t yet figured that out is enormously lucrative. The logic appears to have gotten Zuck's attention. “The SpaceX model I think is quite interesting in terms of just making these short-term deals that are at a big premium,” Zuckerberg told Bloomberg. “So we get offers for all kinds of stuff like this and we’ll evaluate them and see what makes sense.” Reports suggest Meta is seriously considering two strategies for commoditizing its compute assets. The first would be a usage-based compute platform similar to Amazon Web Services' Bedrock. The service would allow customers to run models and serve them through APIs — interfaces that abstract operational complexity. To be clear, Meta already offers API access to its homegrown models, at least the ones it didn’t pull after realizing the way they’d been implemented could be abused. So, from what we gather the difference would be allowing customers to run third party models as well. The second scheme reportedly being explored would involve selling raw compute resources available to end customers — similar to CoreWeave or Lambda. All the right ingredients Meta’s silicon strategy may help as well. One thing all the major cloud providers have in common is a growing catalogue of custom cloud silicon. Once they've identified a core use case, Amazon, Google, and Microsoft all rolled their own silicon to maximize their margins. AWS Trainium, Microsoft Maia, and Google TPUs are all examples of AI accelerators originally built for internal workloads but later made available to the broader public. Meta has been building its own AI chips for years. The first few Meta Training and Inference Accelerators (MTIA) were designed to speed up its recommender models. But new designs, developed in collaboration with Broadcom, are far better suited to running LLMs like Llama and Muse Spark, and whatever else its customers are willing to pay for access to. More importantly, this mix of compute means that Meta can take advantage of the fact GPUs are extremely flexible to bring new products to market quickly. Then once they’ve proven performers, Meta could transition those workloads to its custom chips and offload spare GPU compute to its cloud customers. Meta has all the ingredients, compute, scale, and capital necessary to become a major cloud provider. ®

An AI rewrite of a popular Anthropic-owned JavaScript runtime and toolchain has sparked praise for the speed of its execution, but also criticism of the coding practices behind the project itself. Last week, Bun creator Jarred Sumner announced that he ported Bun from the Zig programming language to Rust in only 11 days, using a fleet of Claude agents running in parallel. The work cost an estimated $165,000 at API pricing, suggesting that software revisions previously considered too large to undertake could actually be feasible now with AI. Sumner said the port was necessary given the growing number of bugs Bun users were finding, including one implicated in the recent Claude Code source leak. But the creator of Zig, Andrew Kelley, didn’t want his project to be seen as the culprit behind Bun’s woes, which he blames on Sumner’s bad programming practices. For Kelley, the move to Rust was not about the feature differences between the two languages, or even the use of AI, but rather “the diverging value systems of the two projects,” he wrote. Bun in the oven Bun is a JavaScript suite consisting of a runtime, package manager, bundler and test runner. Some developers like it because it is a fast one-stop shop that plays well with Node.js. To make Bun speedy, Sumner used Apple's low-memory fast-start WebKit JavaScriptCore (JSC) engine, rather than Google’s stock V8 engine. He used the up-and-coming Zig because he appreciated its performance and low-level control. Anthropic acquired Bun in December 2025. The company built its core state machine on Bun. By then, Sumner had also grown to appreciate AI’s coding abilities, and was using it heavily in the upkeep of Bun. By the time of acquisition, a Claude Bot called RoboBun had been doing a lot of the heavy lifting in the Bun repo. It supplied the most merged PRs of any contributor, fixing bugs and remediating test failures. But as Bun’s user base grew, more cracks started appearing in the code. Users found issues across the software. Anthropic’s 512,000-line code leak in March? That was Bun’s fault, thanks to a bug in the bundler that generated source maps during builds even when told not to, NodeSource reported. All these bugs weren’t Zig’s fault, Sumner explained in a blog post last week detailing the migration. Bun’s architecture mixed garbage collection and application-driven memory management. Sumner admitted that Zig wasn’t designed for that task. Rust was just better at automating memory management. The Rustification of Bun Rewriting 500,000 lines of Zig into another language would be a gargantuan undertaking if done by hand. “A rewrite in another language would take a small team of engineers a full year. It would mean freezing bugfixes, security fixes or feature development for that time,” Sumner wrote. Instead, Sumner went with Claude. He spun up about 50 dynamic Claude Code workflows, reaching a peak of about 1,300 lines of code per minute and generating over a million lines of Rust code. The job took 11 days and cost about $165,000 at API pricing. Claude Fable did most of the heavy lifting. The Rust-based Bun was then subjected to Bun's exhaustive test suite of more than one million assertions. According to Sumner, it passed 100 percent of those tests across all supported platforms without skipping or deleting any. “There’s absolutely no way an engineer with that salary would’ve been able to achieve the milestones Claude did in 11 days,” an impressed HashiCorp co-founder Mitchell Hashimoto noted on X. Zig zags But does Bun’s speed of execution betray the core tenets of good software development? One person not impressed has been Zig’s Kelley, who shared his misgivings in an impassioned post entitled “My Thoughts on the Bun Rust Rewrite." Even before the Anthropic acquisition, “we became increasingly horrified at the programming practices we saw in Bun's codebase,” Kelley wrote. Bun was one of the largest and highest profile projects using Zig and, up until the Anthropic acquisition, a regular financial contributor to The Zig Software Foundation. In Kelley’s view, the project aggressively released new features, resulting in piled-up bugs, bad error-handling code, and technical debt. Sumner “was already writing slop well before he had access to LLMs,” Kelley quipped. He speculated that Sumner may have been under pressure to meet business objectives rather than technical ones, a pressure that increased with Anthropic’s acquisition. In fact, Bun’s codebase had grown so suspect in Kelley’s estimation that Bun parting with Zig was good news. As he put it, no longer would “the publicly presumed poster child for Zig programming language actually [be] the prime example of How Not To Write Zig Code,” he wrote. The Bun team also tried to upstream some of its AI-assisted work to Zig, to no avail. Leading up to the Bun rewrite, the team maintained a fork of Zig that it said improved debug compilation speed fourfold, as eagle-eyed Reg reporter Tim Anderson revealed in May. But the Zig project would not accept Bun’s changes, citing a policy of not accepting AI-based contributions. Zig had been getting an influx of LLM-generated submissions, most of dubious quality. This lack of engineering oversight around AI-generated code would lead to countless problems down the road, Kelley reasoned. Kelley pointed out that if Bun’s tests missed these bugs in Zig, how would they be caught in unsupervised Rust code? “The argument for shipping all the million lines of unreviewed code is that the test suite is good enough to catch everything,” he wrote. “It's not sufficient to catch bugs in Zig code but it is sufficient to catch bugs in [a] million lines of unreviewed slop?” ®

Greg Adams Photography posted a photo:

The footprints fascinated me

Weer voor Rotterdam ☁️ - 14-07-2026 01:15 CEST

In één oogopslag:
• 20.0°C · Gedeeltelijk bewolkt ☁️ | Min 18.1°C / Max 28.3°C

Verwachting voor vandaag:
• Min 18.1°C, Max 28.3°C (Bewolkt) ☁️, 🧭 1019.0 hPa ➡️ 0.0 hPa/24h, Windsnelheid: 19.8 km/u (5.5 m/s), richting: ↙ 38°

Uurlijkse voorspelling voor de komende 12 uur:

• 02:00: 19.9°C (Bewolkt) ☁️, 🧭 1018.7 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 13.3 km/u (3.7 m/s), richting: ↓ 19°
• 03:00: 19.6°C (Bewolkt) ☁️, 🧭 1018.4 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 14.4 km/u (4.0 m/s), richting: ↙ 23°
• 04:00: 19.6°C (Licht bewolkt) 🌕, 🧭 1018.6 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 11.5 km/u (3.2 m/s), richting: ↙ 30°
• 05:00: 19.6°C (Helder) 🌕, 🧭 1018.7 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 12.2 km/u (3.4 m/s), richting: ↙ 38°
• 06:00: 19.4°C (Zonnig) ☀️, 🧭 1018.6 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 12.2 km/u (3.4 m/s), richting: ↙ 40°
• 07:00: 19.6°C (Zonnig) ☀️, 🧭 1018.7 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 15.1 km/u (4.2 m/s), richting: ↙ 42°
• 08:00: 20.7°C (Zonnig) ☀️, 🧭 1018.9 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 17.6 km/u (4.9 m/s), richting: ↙ 48°
• 09:00: 22.1°C (Zonnig) ☀️, 🧭 1019.1 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 15.8 km/u (4.4 m/s), richting: ↙ 45°
• 10:00: 24.0°C (Zonnig) ☀️, 🧭 1019.3 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 15.5 km/u (4.3 m/s), richting: ↙ 54°
• 11:00: 25.4°C (Zonnig) ☀️, 🧭 1019.5 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 16.9 km/u (4.7 m/s), richting: ↙ 66°
• 12:00: 26.4°C (Zonnig) ☀️, 🧭 1019.3 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 17.3 km/u (4.8 m/s), richting: ↙ 65°
• 13:00: 27.5°C (Zonnig) ☀️, 🧭 1019.0 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 16.2 km/u (4.5 m/s), richting: ← 75°

Voorspelling voor de komende dagen:

• woensdag 15 juli: Min 19.4°C, Max 30.1°C (Bewolkt) ☁️, Kans op neerslag 6%, 🧭 1021.3 hPa ↗️ +2.3 hPa/24h, Windsnelheid: 23.8 km/u (6.6 m/s), richting: ↙ 45°
• donderdag 16 juli: Min 20.3°C, Max 28.9°C (Bewolkt) ☁️, Kans op neerslag 1%, 🧭 1019.9 hPa ↘️ -1.4 hPa/24h, Windsnelheid: 19.4 km/u (5.4 m/s), richting: ↙ 48°
• vrijdag 17 juli: Min 20.3°C, Max 27.4°C (Lichte motregen) 🌦️, Neerslag 0.1 mm, Kans op neerslag 3%, 🧭 1016.0 hPa ↘️ -3.9 hPa/24h, Windsnelheid: 13.7 km/u (3.8 m/s), richting: ↙ 24°
• zaterdag 18 juli: Min 17.2°C, Max 24.1°C (Gedeeltelijk bewolkt) ⛅, Kans op neerslag 12%, 🧭 1018.5 hPa ↗️ +2.5 hPa/24h, Windsnelheid: 15.7 km/u (4.4 m/s), richting: ↘ 336°
• zondag 19 juli: Min 16.4°C, Max 22.0°C (Lichte motregen) 🌦️, Neerslag 0.2 mm, Kans op neerslag 6%, 🧭 1023.6 hPa ↗️ +5.1 hPa/24h, Windsnelheid: 17.6 km/u (4.9 m/s), richting: ↘ 329°
• maandag 20 juli: Min 15.9°C, Max 20.0°C (Lichte motregen) 🌦️, Neerslag 0.1 mm, Kans op neerslag 5%, 🧭 1026.2 hPa ↗️ +2.6 hPa/24h, Windsnelheid: 16.0 km/u (4.4 m/s), richting: ↓ 340°

Details:
• 🌡️ Huidige temperatuur (om 01:15): 20.0°C (Gedeeltelijk bewolkt)
• 🤚 Gevoelstemperatuur: 19.9°C (-0.1°C)
• 💨 Windsnelheid: 15.5 km/u (4.3 m/s), richting: ↓ 15°
• 🌬️ Windstoten: 30.2 km/h (8.4 m/s)
• 💧 Luchtvochtigheid: 78%
• 🧭 Luchtdruk: 1018.7 hPa ➡️ 0.0 hPa/3h
• 👁️ Zichtbaarheid: 15.4 km
• ☀️ UV-index: 0.0
• 🌅 Zonsopgang: 05:38 · 🌇 Zonsondergang: 21:57

Luchtkwaliteit:
• AQI: 43 🟢 (Goed)
• PM2.5: 10.6 μg/m³
• PM10: 16.5 μg/m³

Gegevens geleverd door Open-Meteo

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