Xbox imposes harsh new time limits for Game Pass game streaming
Over 1 million Game Pass subscribers will have to pay more for their heavy streaming use.
Latest News Archive - Page 92
Typewise Nova
AI customer experience that builds and improves itself Discussion | Link
Introducing context-aware vulnerability discovery and remediation with Cloudflare Managed Defense and OpenAI Daybreak models
Use production traffic and security signals to prioritize findings, prepare edge mitigations when safe, and propose code patches. By combining WAF data with OpenAI Daybreak models, Vulnerability Discovery and Remediation helps teams identify and patch the most critical threats first.
Startup ARR is less secure than ever, new research shows
The AI era has completely broken enterprise buying patterns, and startups haven't yet figured out how to navigate.
Trump asks Supreme Court to lift block on USPS plan to restrict mail voting
The Trump administration has asked the Supreme Court to lift a temporary block on the U.S. Postal Service plan for President Trump's attempt to restrict mail-in voting for the midterm election.
Multiple trusted publishing configurations for npm
We’re continuing to make trusted publishing smoother for npm publishers, guided by maintainers feedback. Three updates to npm publishing are now generally available: Multiple trusted publishing configurations per package Staged… The post Multiple trusted publishing configurations for npm appeared first on The GitHub Blog .
GitHub Actions: Early September 2026 updates
GitHub Actions now includes three updates that give you clearer visibility and finer-grained control over your workflows. New REST API for runner version deprecations A new REST API returns when… The post GitHub Actions: Early September 2026 updates appeared first on The GitHub Blog .
GPT‑6 Astra
GPT‑6 Astra GPT-6 Astra is "rolling out today to a limited set of organizations and over the coming days will become available to all ChatGPT Plus, Pro, Business, and Enterprise users, as well as through the OpenAI API and AWS" - I've not tried it yet myself, so I don't have a great deal to say about it yet. It's going to be API priced at the same rate as Claude Fable 5 and 5.1: $10/million input and $50/million output. This is clearly OpenAI's Fable competitor, and appears to score higher than Fable on most of OpenAI's self-reported benchmarks. Most impressively, Astra scores 99.9% on the recent (released in March) ARC-AGI 3 benchmark - though notably Fable 5 does not yet have a published result, and the ARC-AGI blog notes that the 99.9% score was achieved for $19K using OpenAI's custom "Provider Adapter harness", while the default ARC-AGI harness scored 62.7% for $26K. The Provider Adapter harness preserves opaque reasoning state between requests and uses compaction for longer conversations, allowing the model to reuse prior work. Unsurprisingly, given the recent Hugging Face incident , Astra is a beast at security tasks. It scores 100% on ExploitBench (GPT-5.6 Sol got 78.5%), 42.4% on ExploitGym (Sol got 30.3%), and 99.2% within four attempts on SRE-Bench binary reverse engineering compared to Sol's 68.7%. It's also better at long context: on OpenAI's eight-needle benchmark it got 100% at 256K–512K tokens and 96.3% at 512K–1M tokens. OpenAI may have vanquished one of the ongoing challenges with long context processing. It doesn't win at everything though. Artificial Analysis note that Astra is still beaten by Fable on their Intelligence Index: Sits beside GPT-5.6 Sol in Intelligence : GPT-6 Astra scores equal to GPT-5.6 Sol in the Index at 61. This is 5 points lower than Claude Fable 5.1 (max with fallback). The model also trails Meta’s newly released Muse Spark 1.3 (max). It did better on their Coding Agent Index: Leads Coding Agent Index cost efficiency frontier : At max effort, GPT-6 Astra costs about the same as GPT-5.6 Sol (max) while scoring 2 points higher on the Index. Per task, the model is less than half the cost of Claude Fable 5, for the same score. I'll write more about Astra once I get access to it. The API model label once it rolls out will be gpt-6-astra . OpenAI's blog keeps throwing 500 errors, but [here's a mirror](https://astratest.codergautam.workers.dev/GPT-6%20Astra_%20A%20new%20generation%20of%20intelligence%20_%20OpenAI) of the post I found [via Hacker News](https://news.ycombinator.com/item?id=49554273#49555070). --> Via Hacker News Tags: ai , openai , generative-ai , llms , llm-release
How to find failures without drowning in tracing data
A metrics dashboard can tell you a system’s health with ease. A log can help you understand a discrete failure. The post How to find failures without drowning in tracing data appeared first on The New Stack .
GPT-6 Astra
OpenAI's most capable model for end-to-end work Discussion | Link
Early Stable Update for Desktop
The Stable channel has been updated to 153.0.8010.27/.28 for Windows. Mac is coming shortly , as part of our early stable release to a small percentage of users. A full list of changes in this build is available in the log . You can find more details about early Stable releases here . Interested in switching release channels? Find out how here . If you find a new issue, please let us know by filing a bug . The community help forum is also a great place to reach out for help or learn about common issues. Srinivas Sista Google Chrome
Forking Cal.com to closed source
This week I'm joined by Peer Richelsen, co-founder of Cal.com. What if the majority of open source repositories are already compromised and we just don't know it yet? That's the theory Peer brings to the table this week. We dig into how AI has flattened the knowledge graph to the point that a 16-year-old can vibe hack a power station just as easily as their mom can vibe code an iOS app, why the reporting culture that has kept open source safe all these years is collapsing under AI generated noise, Cal.com's move to fork its own codebase and take the sensitive parts private, and the eye opening reality that shipping "$1 of AI tokens for pennies on the dollar" is now a common startup business model.
Accel reportedly in talks to lead $1B round for Thinking Machines at $40B valuation
The high-profile startup's annual revenue run rate stands at over $100 million.
Three farmers detained by Israel in Lebanon, village leader says
The detentions were reported to the US-led International Monitoring and Implementation Mechanism but Israeli military has not commented.
GPT-6 Astra is the first model making OpenAI willing to declare the "AGI era"
OpenAI has released GPT-6 Astra, its most capable model yet. President Greg Brockman says it marks the start of the "AGI era." Astra tops benchmarks in math, coding, and cybersecurity and is the first model OpenAI rates as "critical" under its safety framework. During testing, it independently found two previously unknown zero-day vulnerabilities. The article GPT-6 Astra is the first model making OpenAI willing to declare the "AGI era" appeared first on The Decoder .
Enterprise AI transformation relies on the end-to-end platform: Azure was built for this moment
The recognition for Microsoft over the past couple of weeks comes down to models, infrastructure, data, applications, and developer tools working as one system when AI moves into production. The post Enterprise AI transformation relies on the end-to-end platform: Azure was built for this moment appeared first on Microsoft Azure Blog .
siift
Turn AI noise into better business decisions Discussion | Link
GPT-6 Astra
System Card: https://deploymentsafety.openai.com/gpt-6-astra Related ongoing threads: OpenAI's GPT-6 Astra on ARC-AGI-3 - https://news.ycombinator.com/item?id=49555691 GPT-6 Astra makes major gains in the Artificial Analysis Coding Agent Index - https://news.ycombinator.com/item?id=49556147 Comments URL: https://news.ycombinator.com/item?id=49554643 Points: 1904 # Comments: 1722
Abliteration.ai is making a business out of removing AI guardrails
Abliteration.AI is making powerful AI models without guardrails easier to access, arguing that giving defenders the same tools as bad actors could ultimately improve cybersecurity.
Qwen 3.8 27B available on Cerebras at 1500 tokens/s
Article URL: https://inference-docs.cerebras.ai/models/overview Comments URL: https://news.ycombinator.com/item?id=49554520 Points: 583 # Comments: 193
AI data centres are booming in Australia - but at what cost?
Advocates say data centres will bring jobs but others worry they will suck up resources while providing little advantage.
Kubernetes v1.37: DRA Updates
Kubernetes 1.37 is here and Dynamic Resource Allocation (DRA) keeps pushing past where it started! This release brings DRA Extended Resource support to GA, a milestone the team has been building toward for three straight releases. Several more features graduate to Beta or GA. A fresh batch of alpha features rounds out the release. I'll dive into what's new for DRA in Kubernetes 1.37! What's stable in 1.37 DRA Extended Resource support has graduated to GA. This is the mechanism that lets DRA drivers satisfy requests made through the traditional extended resource API, think example.com/gpu in a Pod spec, without requiring a separate device plugin alongside the DRA driver. An extended resource name can be set directly on a DeviceClass, and Pods requesting it get matched to a device through DRA with no ResourceClaim needed on the workload's part. It's been on a steady path since KEP acceptance in 1.34. Alpha landed in 1.35, Beta in 1.36, and now it's Stable. For cluster operators, this is what makes DRA adoption gradual. Existing workloads written against extended resources keep working unmodified while the backend allocation logic moves over to DRA. ResourceClaims status with possible standardized network interface data adds a devices field to ResourceClaim .status , letting DRA drivers report per-device status, including, for network devices, the interface name, MAC address, and IP addresses. This gives users and controllers visibility into device state that was previously invisible once a device was configured in a Pod, and makes it possible to build things like network services that rely on a device's reported IPs. DRA: device taints and tolerations is now Stable; DRA drivers can mark devices as tainted so they're skipped for new Pod scheduling, and cluster admins can apply the same taints cluster-wide via a DeviceTaintRule, without reconfiguring drivers. Pods already using a tainted device can be evicted automatically, unless their ResourceClaim explicitly tolerates the taint. This mirrors node taints and tolerations, letting operators take a single device offline for maintenance or mark it degraded, without disrupting the rest of the cluster. Standard numaNode device attribute standardizes resource.kubernetes.io/numaNode as a shared attribute name, so devices from different drivers can be compared on the same NUMA node instead of each driver inventing its own name for it. It landed directly as stable in 1.37, since it's a naming/registration KEP with no feature gate or in-tree behavior change. Feature promoted to Beta ResourceClaim support for workloads graduates to Beta behind the DRAWorkloadResourceClaims feature gate, which stays disabled by default. In a cluster that has the feature enabled, Workloads and PodGroups can reference ResourceClaims directly, so a single claim can be shared across an entire group of Pods. This is instead of claims being capped at 256 Pods through the old per-Pod reservation limit. The DRA Device Attributes Downward API is aimed at supporting device injection into KubeVirt VMs. Drivers populate a Metadata field when preparing a claim, and the framework writes it to a JSON file mounted into the container via CDI, letting workloads read a device's PCI bus address, MAC address, and other attributes directly instead of requiring custom controllers to watch and translate ResourceClaims and ResourceSlices. Alpha features List types for attributes moved into a second Alpha in 1.37, letting a device attribute hold more than one value instead of a single scalar, such as a CPU that's adjacent to more than one PCIe root. This makes it possible to match or distinguish devices based on overlapping or non-overlapping sets of values, while single-value attributes keep working as they do today. Node allocatable resource requests moved into Alpha 2. It lets the scheduler and kubelet treat DRA-managed CPU, memory, and similar node resources the same way they treat ordinary resource requests, so a node doesn't get oversubscribed and users no longer have to duplicate the same request in both a ResourceClaim and the pod spec. Resource availability visibility moved to a second Alpha in Kubernetes 1.37. Users create a ResourcePoolStatusRequest to get a point-in-time availability snapshot. To refresh it, delete and recreate the request; it is not a continuous monitoring API. DRA: Optional Node Operations lets a driver skip kubelet's prepare and unprepare calls for allocations that don't need any setup on the node. This makes it possible to avoid an unnecessary dependency on the driver for allocations where there's genuinely nothing for it to do locally. Derived Attributes is a new feature that lets you use CEL expressions to match up devices based on your own custom rules. Before this, pairing devices from different vendors (like a GPU/TPU and a NIC on the same NUMA node) only worked if both drivers used the exact same attribute name. If one used numa and the other used numaNode , the scheduler couldn't pair them together. Now, you can easily bridge these differences yourself inside your manifest, meaning you don't have to wait for hardware vendors to agree on standardized attribute names. Beyond just fixing naming differences, you can also use CEL to handle more complex scenarios like slicing a specific ID out of a long, monolithic topology string, or grouping devices into custom performance tiers based on their available capacity. DRA Device Compatibility Groups lets drivers tag partitions of a device, like MIG vs vGPU profiles on the same GPU, with compatibility groups, so the scheduler rejects incompatible combinations up front instead of the driver failing at node preparation time. It's controlled by the DRADeviceCompatibilityGroups feature gate, disabled by default. PreQueueingHint extension point is new as Alpha in 1.37. DRA ResourceClaim events used to trigger a full scan of every unschedulable pod, an O(N²) cost during large scale-ups. The DRA plugin now uses a pod informer index to narrow that to just the pods actually affected, cutting the requeue path to O(1) and roughly doubling scheduling throughput in early benchmarks. Controlled by the SchedulerPreQueueingHints feature gate. DRA Consumable Capacity now supports fractional values in CapacityRequestPolicyRange, enabling more precise capacity requests and allocation for devices with fractional resources. This improves flexibility for workloads that require fine-grained resource allocation. The enhancement is gated by the DRAFractionalCapacityRange feature gate, which is in Beta in 1.37. What’s next DRA continues to mature with every release. Several features currently in Alpha and Beta are on track to progress in the coming releases, and the community keeps working on DRA's performance, scalability, and reliability. Expect another ambitious set of DRA features in Kubernetes 1.38. Getting involved A good starting point is joining the WG Device Management Slack channel and meetings which happens at US/EU and EU/APAC friendly time slots. Not all enhancement ideas are tracked as issues yet, so come talk to us if you want to help or have some ideas yourself! We have work to do at all levels, from difficult core changes to usability enhancements in kubectl which could be picked up by newcomers. Acknowledgments The following KEP owners added or promoted a feature in the 1.37 release (in alphabetic order): Alay Patel ( alaypatel07 ) Byonggon Chun( bg-chun ) Gaurav Ghildiyal ( gauravkghildiyal ) Jiefeng Xu ( jiefeng-xu ) John A. Hull ( johnahull ) Jon Huhn ( nojnhuh ) Lionel Jouin ( LionelJouin ) Patrick Ohly ( pohly ) Praveen Krishna ( pravk03 ) Shingo Omura ( everpeace ) Troy Chiu ( troychiu ) This would not have been possible without the help of the reviewers and approvers. So a huge thanks to everyone else who helped shape this release, in ways big and small. Given enough eyeballs, all bugs are shallow and this release had plenty of them, watching closely and caring enough to make things better. DRA got better this cycle because of all of you.
GPT-6 Astra: Frontier intelligence for work, now generally available in Microsoft Foundry
GPT-6 Astra, OpenAI's newest frontier model, begins rolling out today through the Microsoft Foundry Limited Access Program, with availability expanding to participating customers over the coming days. The post GPT-6 Astra: Frontier intelligence for work, now generally available in Microsoft Foundry appeared first on Microsoft Azure Blog .
Pangram's biggest flaw is users turning its scores into public shaming
Pangram hired an "attack dog" to shame alleged AI users on social media. But the campaign blurs two things that aren't the same: Pangram only somewhat reliably measures whether AI was used, while the shaming implies the person didn't think or work on their own. A high AI score hits a text built on hours of original research just as easily as one cranked out from a ten-second prompt. The article Pangram's biggest flaw is users turning its scores into public shaming appeared first on The Decoder .