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Microsoft published two Defender vulnerabilities on May 19, 2026 that are being actively exploited in the wild, and CISA has already pushed both into the Known Exploited Vulnerabilities catalog. If you run Windows endpoints, this is a same-week update item, not a "schedule it for the next maintenance window" item. The patches exist, the abuse is happening, and the BOD 22-01 deadline for federal civilian agencies is June 3, 2026. what follows: what happened, who needs to act, and what to do today before someone else makes the decision for you. What's being exploited CVE-2026-41091 is an Elevation of Privilege bug in Microsoft Defender's scanning logic, rated Important. The root cause is improper link resolution before file access. An authenticated local attacker plants symbolic links or NTFS junctions that point at attacker-controlled paths, then triggers Defender to follow them. Defender operates with SYSTEM privileges during scan operations, so the file actions Defender performs on those crafted targets execute as SYSTEM. Net result: a non-admin local user gets full SYSTEM on the host. The attacker needs an authenticated session already. That sounds like a high bar until you remember that initial-access malware lands at user-level, then chains a local privilege escalation to get persistence and lateral-movement capability. CVE-2026-41091 is the second-stage tool intrusion sets are looking for. The Hacker News and BleepingComputer both confirm the in-the-wild abuse is happening. CVE-2026-45498 is a Denial of Service in the Microsoft Defender Antimalware Platform itself. Attackers can trigger a platform-level crash that takes Defender's protection capabilities offline. The exploitation pattern here is the obvious one: kill the EDR/AV before deploying the actual payload, get a clean window for follow-on actions, restore Defender or leave it broken depending on how careful the operator is. CISA's KEV listing tells you this is being chained operationally, not a theoreti

Can an AI decide that it's going to nefariously drive the human it's interacting with slowly insane? submitted by /u/Numerous-Cup1863 [link] [留言]

The Framing Problem When industry analysts discuss "AI CAD," they are frequently conflating two fundamentally different computational paradigms: generative mesh synthesis and parametric feature modeling. This conflation has produced a decade of inflated expectations, underwhelming demos, and a persistent belief that real AI CAD is still "coming." It is not coming. For a specific and technically meaningful definition of AI CAD, it has arrived. Mesh Generation vs. Parametric Modeling: Why the Distinction Is Everything Contemporary generative 3D tools including neural radiance field reconstructions, diffusion-based mesh generators, and implicit surface networks produce geometry as an unstructured point cloud or polygon mesh. These representations are geometrically expressive but engineering-inert. They carry no feature history, no constraint graph, no dimensional intent. A mesh cannot be toleranced. A mesh cannot propagate a design change. A mesh cannot be submitted to a manufacturer without full reconstruction from scratch. Parametric CAD, by contrast, encodes design intent as a structured sequence of operations — extrusions, revolves, fillets, boolean operations each governed by explicit dimensional constraints and parent-child dependency relationships. The parametric model is not merely a shape; it is a design process, replayable, modifiable, and transferable across manufacturing contexts. The meaningful technical question for AI CAD in 2026 is therefore not " can AI generate a 3D shape? " that has been demonstrable since 2019. The question is: can AI generate a valid parametric feature tree from natural language input, with embedded manufacturing constraints, that survives downstream engineering use? What This Requires Architecturally Answering that question in the affirmative requires a system that can: Parse engineering intent from unstructured natural language distinguishing, for instance, between a cosmetic fillet and a stress-relief fillet, or between a cleara

submitted by /u/esporx [link] [留言]

On August 19, Startup Battlefield is returning to Sydney in partnership with Stripe, one of the world's most iconic technology companies. We're taking over Stripe Tour Sydney for a night that the Australian startup ecosystem won't forget.

These to seem to be confused and mixed up often. How do you pick those apart? submitted by /u/phamsung [link] [留言]

Paying users will have tools for reaching either wider or more-specific audiences.

Estonian government benchmark shows how dozens of models combat Russia's "strategic narratives."

Meta was previously reported to be exploring facial recognition for its smart glasses.

submitted by /u/WrongdoerRough4712 [link] [留言]

DIYer and TikTok user Annike Tan, who goes by @ubeboobey, can carry her cyberdeck around without anyone noticing because it doesn't look like a computer at all. Tan, who has been featured in The Cut and Wired, went viral earlier this year with a mermaid-themed cyberdeck she made inside an old purse. She has since […]

Hi, everyone I am working as a Software engineer. The past few years I oversleep a little bit in scope of AI mostly because I am sceptical about it. I decided that I would like to move on and be more up to date with it and potential use of it. How do you use it in day to day habits or work? How to monetize it? submitted by /u/Blvckhype [link] [留言]

By targeting large numbers of users, attackers increased their chances of success.

The native macOS file manager Finder should be Discussion | Link

Musk can't be trusted to protect X user privacy, public commenters warn FTC.

Meta may have found one way to slash its massive data center bill: tents.

For those doing heavy AI programming or running local models on mobile hardware: Is the current generation of iPhone Pro or Samsung Galaxy Ultra actually making a difference in your workflow, or is it mostly a gimmick right now? submitted by /u/Spirited_Good9789 [link] [留言]

Poke, the startup that lets people use AI agents through simple text messages, has become the first AI agent approved for Apple’s Messages for Business platform.

Fusion startup Helion is racing to complete a power plant for Microsoft by 2028. A fresh infusion of cash should help with that.

Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product. What if you could change how an AI thinks without retraining it? Not by rewriting prompts. Not by fine-tuning billions of parameters. Not by collecting another mountain of training data. Instead, imagine finding a direction inside the model's internal representation space and nudging the model a little in that direction. A small push. A different behavior. This idea sits at the heart of one of the most fascinating areas of modern AI interpretability: steering vectors . Steering vectors suggest that many behaviors we care about—careful reasoning, honesty, coding style, security awareness, verbosity, and more—may already exist inside a model. The challenge is learning how to activate them. Let's explore what steering vectors are, how they're created, and why they might become one of the most practical tools for controlling AI systems. 1. What Exactly Is a Steering Vector? Large language models process information through layers of high-dimensional activations. At any point during generation, the model's internal state can be represented as a vector containing thousands of numbers. Researchers discovered something surprising: Different behaviors often correspond to different regions of this activation space. For example: Writing Python code Solving math problems Speaking French Explaining concepts carefully Producing insecure code Each tends to produce distinctive activation patterns. A steering vector is essentially the difference between two activation patterns. Suppose we gather examples where the model is: Careful Methodical Thorough and compare them to examples where it is: Rushed Superficial Incomplete The average difference between these internal states becomes a steering vector. At inference time, we can add that vector back into the model's activatio