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Square-tailed Kite
Scientific Name: Lophoictinia isura
Although it usually occurs singly, the Square-tailed Kite is sometimes seen soaring in pairs during the breeding season, and family groups of adults and one or two dependent young may be seen during post-fledging period. The Square-tailed Kite usually hunts by flying low over the treetops, occasionally plunging down through the foliage to snatch a bird or insect from among the leaves or twigs. The species often eats the nestlings of birds, and sometimes it will remove the entire nest to get at the young birds, and at other times may remove the tiny birds, one clutched tightly by the talons of each foot. They also catch adult birds by surprising them in the canopy of the forest.
Description: Often solitary, but can be seen in pairs when nesting. Squared-tailed Kites have a long, square tail with very long, upswept paddle-shaped wings and a large cream crescent at the base of their wing tips.
Similar Species: Immature Black Kite, Black-breasted Buzzard, and Red Goshawk
Distribution: Endemic to mainland Australia.
Habitat: The species mainly inhabits open eucalypt forests and woodlands, often where there is a broken canopy, but it also ranges into nearby open habitats. In southern Australia, Square-tailed Kites mainly inhabit open eucalypt forests and woodlands, often dominated by stringybarks, peppermints or box–ironbark eucalypts, as well as Woollybutt, Spotted Gum, Manna Gum, Messmate, River Red Gums, as well as other trees such as Angophora, cypress-pines and casuarinas. It also occurs along the edges of dense forest and along in road verges with remnant or planted trees, and in clearings within forest or in areas of regrowth, up to 4 years after the area has been logged. Other habitats which occasionally support Square-tailed Kites include mallee, heathland (mallee or coastal) and other low shrublands including saltbush plains, and also grasslands or open or cultivated farmland near remnant woodland.
Feeding: Searching for prey from the air, where they are highly agile at low levels, they mainly hunt in eucalypt open forest or woodland, and less often in low shrublands, heath, grassland or crops, and the margins between open and timbered country (forest–heath; woodland–heath; forest–open field; mallee–open paddocks; woodland edges; riparian timber; belts of trees in urban or semi-urban areas; and clearings in forests) are especially favoured. They specialise in hunting among trees, twisting between and below tree-tops, and they take most prey from the outer foliage of the canopy, but do not enter the canopy.
Breeding: Square-tailed Kites nest on horizontal branches in mature living trees, especially eucalypts, often near water, and they need extensive areas of forest or woodland surrounding or nearby.
Calls: Yelping, yeep, yeep, yeep. Also squealing ee ee ee ee
Minimum Size: 50cm
Maximum Size: 55cm
Average size: 53cm
Average weight: 568g
Breeding season: Aug - Dec
Incubation: 37 days
Nestling Period: 63 days
(Sources: www.birdsinbackyards.net/species/Lophoictinia-isura and www.birdlife.org.au/bird-profile/square-tailed-kite)

© Chris Burns 2026
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A decade ago, when I was still speaking at conferences and heavily marketing myself, I’d often think of personal branding ideas. We’ve seen the trends. The business cards. The t-shirts. The stickers. The buttons. The socks! Can’t forget the socks. My logo is branded across not only my sites but my computers and my slide decks, too.

I also liked using colour to reinforce my brand. I’ve continued that tradition with green threading throughout the latest redesign. I even considering wearing green for all my conference talks. Thankfully, that ended up being a short-lived idea.

At one point in my career, I was getting interviewed for a number of podcasts and considered starting up my own podcast. One of the ideas that came out of preparing to start my own podcast was how to brand via audio. I liked the idea of doing chimes like the NBC chimes so that’s what I did:

It’s five dings in the pattern of how I spell out my last name: Ess Enn oh-OH Kay.

Had I implemented this, I had considered using it to intro my conference talks. I likely would’ve integrated it into the site somehow as well. Maybe clicking on the logo rings the chime as the letters of my name pop in sequence.

For now, I’ll simply document this little artifact of time.

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Have something to say? Tell me about it.

AI-assisted vulnerability discovery has yet to produce the expected surge in real-world attacks: VulnCheck found that only 14 of 1,061 attributed discoveries, or 1.3 percent, had been exploited, which is "almost identical to the rate across all vulnerabilities in VulnCheck's dataset," reports The Register. "That's a far cry from the narrative that frontier AI is dramatically tilting the balance in attackers' favor by churning out instantly weaponizable bugs." The findings suggest AI is currently better at increasing the volume of bugs found than making them easier to weaponize. From the report: The report takes particular aim at Anthropic's much-publicized Project Glasswing, unveiled in April with warnings that AI-assisted vulnerability discovery could allow attackers to hijack systems, disrupt operations, or steal data. Claude Mythos may have identified 23,019 vulnerability candidates, but there's remarkably little public evidence showing what became of most of them. VulnCheck notes that only 126 have been published as CVEs, that just one has been confirmed exploited in the wild, and that Anthropic's public disclosure record has seen little movement since Project Glasswing launched.

But that doesn't mean AI-assisted vulnerability research has failed, according to Patrick Garrity, security researcher at VulnCheck. "AI-assisted vulnerability discovery clearly has value for both attackers and defenders," Garrity wrote. "The data does not suggest that AI-discovered vulnerabilities are inherently more likely to be exploited than those found through traditional methods." Instead, he argues, AI is simply helping researchers discover more flaws, giving defenders an opportunity to patch them before criminals get there.

Garrity stopped well short of declaring the threat overblown forever, but he did suggest some of the rhetoric has outpaced reality. "The data so far, including Anthropic's own stalled disclosure ledger, suggests that AI-assisted vulnerability discovery and frontier capabilities have been overhyped relative to the evidence available today," he wrote. "That doesn't mean the risk is imaginary. It means the impact has been real but modest."

Read more of this story at Slashdot.

We wandelen langs het Planetenpad bij het Herdenkingscentrum Westerbork en verdiepen ons in de op schaal geplaatste informatieborden over de planeten in ons zonnestelsel.

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?

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

Wie of wat staat er midden in het nieuws? Een actuele puzzel, die makkelijker is als je het nieuws een beetje volgt.

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

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

History is littered with the corpses of technologies that were ahead of their time, and Intel’s Optane storage and memory products are certainly among them. Expensive, badly misunderstood, and awkwardly priced, the technology struggled to find a place in the market, surviving just five years before Chipzilla pulled the plug. Things might have been different if it were launched today. The shift from AI training to inference has driven tremendous demand for DRAM and NAND flash. In fact, the same properties that made Optane a niche product a few years ago would have made it ideally suited to these write intensive AI workloads. The rise and fall of Optane Intel and Micron co-developed Optane – or more specifically, 3D XPoint memory – and unveiled it in 2015. It promised to bridge the gap between conventional NAND flash used in SSDs and DRAM used in DDR4 and (later) DDR5. Like NAND, 3D XPoint was non-volatile, which means data persists when unpowered, making it appropriate for storage applications. But unlike NAND, 3D XPoint didn’t store data using trapped electrons and instead relied on a phase change material that was both extremely fast, achieving sub-10-microsecond latencies (even lower for later PMem modules), and absurdly write-endurant. Intel’s penultimate Optane SSDs boasted endurance of 100 drive writes a day and a mean time between failures of two million hours — specs that remain unrivaled today. Those figures are of course extrapolated, based on what we know about how 3D XPoint reads and writes data. But if anything, we suspect Intel was probably being conservative with its claims. This one-two punch of endurance and latency, particularly for random reads and writes meant that it could be used as a second tier of system memory. Optane SSDs are really, really low latency for storage-class memory, but they’re still orders of magnitude less responsive than DRAM which can hit 100 nanoseconds or lower. We should note Intel’s PMem DIMMs were capable of latencies of roughly 350 nanoseconds. Intel’s Optane persistent memory also had the benefit of capacities up to 512 GB per DIMM, at a time when the most you could expect out of DDR4 was about 128 GB – and only if you had deep pockets. These persistent memory DIMMs could be made to behave like main memory, with standard DDR4 functioning as a massive L4 cache when enabled; as a storage pool; or in an application-aware memory mode, which exposed the Optane memory directly to select applications. Despite Optane’s many strengths, it was rather awkwardly priced. For the same memory density, 3D XPoint was consistently more expensive than NAND and, while cheaper than DRAM, significantly less performant. As a result, there was rarely a scenario in which users were better off with Optane than they would be just buying more NAND or fewer, bigger DIMMs. To make matters worse, as the boffins at TechInsights noted at the time, while 3D XPoint had its merits, it wasn’t improving quickly enough to keep up with NAND flash on bit density. By 2021, Micron had had enough. The memory vendor announced it would end development of 3D XPoint products. With no source of new silicon for its SSD and persistent memory products, the writing was on the wall for Optane. Intel's then-CEO Pat Gelsinger officially pulled the plug in 2022, ending development of new products under the Optane banner. The P5810X and P5811X, the final Optane products to roll off the assembly line, launched in late 2022. Ahead of its time The same year Intel pulled the plug on Optane, a new workload was emerging that would completely turn the datacenter on its head, and might very well have been the killer app to justify 3D XPoint’s continued development. In November 2022, OpenAI, then a relatively obscure AI startup, lifted the curtain on ChatGPT, kicking off an AI arms race and sparking a massive influx of money into the tech sector. Large language models are among the most resource-intensive workloads in the world. But until 2025, most of that compute was dedicated to training bigger, smarter, and less hallucination-prone models. The arrival of DeepSeek early in that year signaled the shift toward inference, a workload that needs plenty of compute, memory, and storage. Modern inference engines are tuned to maximize efficiency. One way is by caching the key value pairs used to track model state. Chatbots and agents are inherently iterative. For multi-turn sessions, every prompt contains not only new information, but also every request and response that came before it. Recomputing all of that every time a new request is made is wasteful, so instead that information is computed, cached, and reused. The problem is at large context sizes and high concurrency, these KV caches can get rather large. A single 64,000-token sequence in a model like DeepSeek R1 can chew up four gigabytes of GPU memory. Multiply that across hundreds or thousands of users and it adds up quickly. Modern GPUs don’t have much memory, and what they do have is usually tied up hosting model weights. Many inference engines therefore support KV cache offloading, either natively or through plugins. Once GPU memory is exhausted or chat sessions stale, older chats are ejected to system memory. But DRAM is expensive, in short supply, and may not even be enough. In fact, Nvidia is reportedly cutting the amount of LPDDR5X shipped as part of its Vera Rubin platform. Because of this, KV caches where, for example, a user has walked away and the session has been idle for an hour or more, may be offloaded to flash storage arrays for longer-term storage. The problem is that KV caching is a write-intensive workload and NAND has a finite number of writes it can make before it’s shot. There's a lot of work being done to mitigate this, but the fact remains that if you write enough data to NVMe storage, eventually it wears out. Optane’s otherworldly write endurance and low latency, particularly when concerning small random writes which dominate KV-caching, would have made it a perfect choice for this workload. Optane’s successors One of the nails in Optane’s coffin was that Compute Express Link (CXL) was already on the horizon. If the primary reason for adopting Optane was to get your hands on a large pool of reasonably fast DRAM-like memory, then CXL offered all the benefits and none of the compromises. In effect, early CXL implementations were essentially a remote memory controller to which you could attach your choice of memory, DDR4 or DDR . Need more memory than your CPU supports? Just plug a CXL memory expander into a free PCIe slot and you were off to the races. Later revisions of the memory coherent protocol added support for pooling, and later sharing. However, CXL hasn’t changed the fact that DDR5 is expensive at the best of times, and we are currently living in the worst of times for DRAM prices. (With that said, while your CPU may only support DDR5, the CXL controller might still be able to use DDR4. This is exactly what Meta is doing to boost the memory capacity of its systems on the cheap.) In the absence of Optane, memory vendors have attempted to fill the void with write-optimized NAND of their own. Kioxia’s XL-Flash is one such example of storage class memory (SCM) that promises many of the same benefits as Optane, including advertised write latencies under 10 microseconds, and endurance up to 60 drive writes a day, through the use of SLC (1-bit per cell) memory. There’s also a crossover with CXL here. XL-Flash can be exposed as a CXL device to simplify memory tiering. Samsung also attempted to replicate many of Optane's qualities with its Z-NAND tech, but the tech still falls well short of 3D XPoint. Samsung is reportedly revamping the tech and aiming for a 15x performance increase over conventional NAND. Alas, neither quite lives up to Optane’s legacy. The tech was simply too far ahead of its time. Had Intel and Micron waited just a few years longer, it might have ridden the AI wave all the way to victory. ®