Cybersecurity researchers have disclosed details of fraudulent activity targeting users across the Middle East and North Africa by employing various fraudulent Facebook accounts impersonating politicians, public figures, and trusted organizations. "These accounts promoted fake offers, including free mobile internet packages, financial compensation, and government subsidy programs," Group-IB
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Palo Alto Networks has revealed that it has observed "active exploitation" of a recently disclosed PAN-OS vulnerability by an unknown threat actor to obtain unauthorized access to GlobalProtect portals. The vulnerability in question is CVE-2026-0257 (CVSS score: 7.8), an authentication bypass flaw affecting the portal and gateway components of PAN-OS software that could be exploited by bad
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Article URL: https://daniel.haxx.se/blog/2026/06/15/curl-summer-of-bliss/ Comments URL: https://news.ycombinator.com/item?id=48537165 Points: 619 # Comments: 250
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Article URL: https://platform.claude.com/docs/en/cli-sdks-libraries/libraries/apple-foundation-models Comments URL: https://news.ycombinator.com/item?id=48536776 Points: 338 # Comments: 148
Orbio announces $21 million Series A in round led by Dawn Capital.
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Asian tech stocks surged Monday on news that Iran and the U.S. have reached a deal.
What's Changed llama: update llama.cpp to b9637 by @jmorganca in #16609 Full Changelog : v0.30.8...v0.30.9-rc0
Article URL: https://karthinks.com/software/even-more-batteries-included-with-emacs/ Comments URL: https://news.ycombinator.com/item?id=48535886 Points: 288 # Comments: 90
[...] Instead, I picture a specific person and I just write for them. Often this person is "me, but 3 years ago" or a good friend. — Julia Evans , write for 1 person Tags: writing , julia-evans
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A government order abruptly took down a powerful AI model, exposing a new kind of supply chain risk for engineering teams. Security leaders need contingency plans before the next model disappears.
In our ongoing SIG Spotlight series, we shine a light on the groups that keep the Kubernetes project moving forward. This time, we catch up with SIG Storage , the group responsible for persistent data, volume management, and the interfaces that connect Kubernetes workloads to the storage systems beneath them. We spoke with Xing Yang , Co-Chair of SIG Storage and Software Engineer at VMware by Broadcom, about the SIG's history, the features shipping in recent Kubernetes releases, and where storage in Kubernetes is headed as AI workloads become the norm. Introductions Could you introduce yourself and share your role(s) within SIG Storage? My name is Xing Yang , a software engineer at VMware by Broadcom. I'm a co-chair in SIG Storage, alongside another co-chair Saad Ali from Google. There are also two Tech Leads in SIG Storage: Michelle Au from Google and Jan Šafránek from Red Hat. What first drew you to storage in Kubernetes, and how did you start contributing? I have always been working in the storage domain, so SIG Storage was a natural place for me to get started when I began to learn Kubernetes. I started attending SIG Storage meetings , trying to figure out what I could do to help. This was before the first Container Storage Interface (CSI) release — lots of things were still evolving. It was a very exciting time. What subprojects or areas do you actively maintain or review today? I'm a maintainer in Kubernetes CSI. There are multiple CSI sidecars — such as csi-provisioner , csi-attacher , csi-resizer , and csi-snapshotter — that we need to release following every Kubernetes release. I'm also a co-chair for a Data Protection Working Group co-sponsored by SIG Storage and SIG Apps . Several features have come out of that WG aimed at filling gaps in data protection support within Kubernetes. One is Volume Group Snapshot , which provides crash-consistent group snapshots for multiple volumes used by an application. Changed Block Tracking (CBT) is another critical feature from the DP WG designed to support efficient backups. About SIG Storage For folks who are new: what is SIG Storage, in your own words? What problems in Kubernetes are you trying to solve? SIG Storage is a Special Interest Group focused on how to provide storage to containers running in your Kubernetes cluster. We define standard interfaces so that a storage vendor can write a driver and have its underlying storage system consumed by containers in Kubernetes. Why does Kubernetes need a dedicated storage SIG? What makes storage hard in a distributed system? When Kubernetes was first introduced, it was meant for stateless workloads only. Container applications were regarded as ephemeral and therefore did not need to persist data. However, that changed drastically. Stateful workloads started running in Kubernetes, and we needed a dedicated SIG to tackle the associated storage challenges. PersistentVolumeClaims, PersistentVolumes, and StorageClasses were all introduced to provision data volumes for applications running in Kubernetes. How did SIG Storage originally form, and how has its mission changed over time? SIG Storage was formed to address the challenges of handling persistent data within Kubernetes. Initially, PersistentVolumes were implemented as in-tree plugins, and the SIG managed those plugins while developing core storage primitives like PersistentVolumes and PersistentVolumeClaims. Container Storage Interface (CSI) was introduced later and played a crucial role in simplifying storage integration, enabling third-party storage providers to develop and maintain their own out-of-tree plugins without modifying Kubernetes core code. With basic integration addressed by CSI, the SIG's mission expanded to include advanced storage features that leverage the new interface. The SIG has also expanded its scope to support object storage through the Container Object Storage Interface (COSI). Current work and roadmap What are the top features SIG Storage is actively working on right now? The Data Protection WG has been working on a couple of exciting features: VolumeGroupSnapshot is a Kubernetes feature enabling a crash-consistent, point-in-time snapshot of multiple PersistentVolumes simultaneously. This ensures data integrity for applications — like databases — that rely on multiple volumes by capturing all volumes in the group atomically, at the exact same point in time. It just moved to GA in Kubernetes v1.36. CSI Changed Block Tracking (CBT) enables efficient, incremental backups. By allowing storage systems to report only the blocks that have changed since the last snapshot, it significantly reduces the amount of data that needs to be transferred. It just moved to Beta in Kubernetes v1.36. Another feature worth highlighting is Container Object Storage Interface (COSI) . COSI provides a standard interface for provisioning and consuming object storage buckets in Kubernetes — standardizing object storage for containerized applications much like CSI did for block and file storage. COSI is now transitioning to v1alpha2 , with plans for promotion to Beta in a future release. What recent work from SIG Storage do you consider a "win" for users? The graduation of VolumeAttributesClass to GA in Kubernetes v1.34 is a major win for users managing stateful workloads. Previously, changing volume attributes like IOPS or throughput required out-of-band actions or disruptive operations. Now, users can dynamically tune storage properties such as IOPS or throughput directly through the Kubernetes API — scaling up for peak loads or down to optimize costs — without external processes or downtime. VolumeAttributesClass enables dynamic modification of storage characteristics without recreating the volume. This completes the picture by allowing users to tune both capacity and other storage properties dynamically, just as they can now tune both CPU and memory for compute. Looking ahead one or two releases, what's on the roadmap that people should watch for? I'd like to draw attention to the Volume Health feature. This feature is designed to offer critical visibility into the operational status and integrity of persistent volumes. By enabling storage drivers and the Kubernetes control plane to report issues, it allows for proactive monitoring and identification of volume-related problems. Currently, volume health information is reported via non-persistent events. We are actively investigating enhancements to this feature with the goal of supporting automated remediation capabilities in the future. Are there areas where you'd really like more discussion or help from the community? We always need help from the community to fix bugs, add tests, and help with reviews. We'd also like to get feedback on the Alpha feature Mutable PV Affinity , which was introduced in Kubernetes v1.35. Use cases include migrating volumes from zonal to regional storage or migrating from one disk type to another. Another topic is volume replication . It was raised at KubeCon Atlanta and has been discussed in the Data Protection WG. Community members interested in this topic are encouraged to join the DP WG meetings. What are the biggest challenges users face today when running stateful workloads on Kubernetes? While Kubernetes has moved stateful workloads — like databases and AI pipelines — into the mainstream, managing "state" in a system designed for ephemerality remains difficult: Data Gravity and Storage Locality : Pods move in seconds, but data has gravity. If a node fails, a pod using local storage is stuck. Operators must decide whether the failure is transient or permanent — a high-stakes call. This is why we are enhancing the Volume Health feature to provide the visibility needed to automate recovery choices. Day 2 Complexity : Setting up a database is easy; maintaining its health over time is the real challenge. Standard Kubernetes objects like StatefulSets offer a baseline, but they lack the operational logic needed for tasks such as schema upgrades, engine patching, or cluster-wide Kubernetes upgrades. Data Mobility : Moving persistent data remains a significant hurdle — whether migrating between storage tiers, shifting workloads across availability zones, or moving to a different cluster. This challenge includes ongoing synchronization and replication for high availability and disaster recovery across a distributed system. Storage and AI How do you see storage evolving in Kubernetes over the next few years, especially as AI/ML workloads grow? I see several trends shaping storage in Kubernetes as it evolves from a container orchestrator into the "Operating System" for AI: More Intelligent Data Management : We'll see a shift toward smarter CSI drivers and data management tools offering advanced features like automatic tiering, snapshots, migration, and replication — optimized specifically for high-performance AI/ML workflows and large data platforms. Object Storage as a First-Class Citizen : AI datasets now frequently reach exabyte scale, making object storage the preferred choice for AI workloads. COSI is standardizing bucket management just as CSI did for disks, allowing data scientists to use a BucketClaim to provision S3-compatible storage natively and unifying object, file, and block storage into a single workflow. Performance and Low Latency : For AI/ML, storage needs to keep up with GPU processing speeds. This will accelerate adoption of high-performance parallel file systems and NVMe-over-Fabrics (NVMe-oF) technologies managed natively via Kubernetes. The line between traditional block/file and memory-speed storage will continue to blur. Data-Aware Scheduling : Instead of just considering CPU and RAM, the Kubernetes scheduler will increasingly prioritize placing Pods based on data locality — calculating the cost of moving data versus moving compute to keep massive data platforms performant. SIG Storage continues to tackle some of the hardest problems in Kubernetes: keeping stateful applications running reliably, making storage operations transparent and composable, and now scaling up to meet the demands of AI-era workloads. Whether you're a user managing databases in production or a developer curious about storage internals, there's a place for you in SIG Storage. If you'd like to get involved, check out the SIG Storage community page and join the bi-weekly meetings . You can also find the SIG on Slack at #sig-storage . SIG Storage Mailing List SIG Storage on Slack Data Protection WG
Why AI hasn’t replaced software engineers, and won’t Arvind Narayanan and Sayash Kappor take on the question of AI job losses through the lens of a profession that is uniquely suited to AI disruption - software engineering. In this essay, we argue that there is enough evidence to reject the narrative that once AI capabilities reach a certain threshold, it will cause mass layoffs. Given that this is true even in a sector with very few regulatory barriers, most other professions are likely to be even more cushioned. The first good news is that the data still doesn't support the idea that AI is causing mass unemployment. In March 2025, New York became the first U.S. state to add an AI disclosure checkbox to WARN Act filings. In the full first year, more than 160 companies filed WARN notices. Not a single one checked the AI box AI speeds up the typing-code-into-a-computer phase, but it turns out software engineering is about a whole lot more than that: If writing code isn’t the bottleneck, what is? The task-breakdown surveys point at things like meetings or debugging. This just leads to more questions: what are developers doing in those meetings and why can’t it be done by AI? Won’t debugging get automated as capabilities improve? To understand the real bottlenecks, we have to get qualitative, and dig into software engineers’ own understanding of what it is they do that resists automation. When we did this analysis, it revealed three things as the real bottlenecks (1) deciding and specifying what to build, (2) verifying and being accountable for what is delivered, and (3) the deep human understanding — of the codebase, the business, and the environment — required to carry out both of these. I'm finding AI assistance also helps me with the deciding and verifying steps, but it's the "deep human understanding" that remains key to the value I provide. Give me all of the AI assistance in the world and the value I produce will still be reliant on how deeply I understand both the problems and the solutions that the agents are building for them. Tags: careers , ai , generative-ai , llms , arvind-narayanan , ai-ethics
Privacy-friendly web analytics with realtime AI insights Discussion | Link
Under the agreement, the key Strait of Hormuz waterway will be reopened, US President Donald Trump said.
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UK drivers have taken a shine to the SUV but could the fate of the convertible be reversed?
Article URL: https://andreklein.net/your-epub-is-fine-kobo-disagrees-blame-adobe/ Comments URL: https://news.ycombinator.com/item?id=48533848 Points: 697 # Comments: 233