A former U.S. Army soldier has been sentenced to 70 months in prison for hacking and extorting at least 10 U.S. technology and telecommunications companies between April 2023 and December 2024.
21-year-old Cameron John Wagenius (also known online as ‘kiberphant0m’ and ‘cyb3rph4nt0m’) was arrested in Texas in December 2024.
He pleaded guilty in February 2025 to hacking AT&T and Verizon after being charged on two counts of unlawfully transferring confidential phone records, and in July 2025 to multiple counts of aggravated identity theft, conspiracy to commit wire fraud, and extortion related to computer fraud.
The ShinyHunters extortion gang is using a URL-encoding trick to bypass web application firewall rules that mitigate the Oracle PeopleSoft CVE-2026–35273 flaw, allowing the threat actors to resume widespread exploitation of a flaw on vulnerable servers.
Google’s Mandiant and Threat Intelligence Group (GTIG) say this new technique has allowed the threat actor to once again target PeopleSoft servers that had not applied security updates and instead blocked access to the vulnerable PSEMHUB endpoint using a WAF.
On June 10, BleepingComputer first reported that the ShinyHunters extortion gang was targeting Oracle PeopleSoft servers using a zero-day vulnerability, allowing them to steal data from 100 organizations.
Cloudflare has fixed a vulnerability in Containers and Sandboxes that allowed customers with a Workers Paid account to recover residual data from other customers’ containers on the same physical host.
Cloudflare Containers is a service available on the Workers Paid plan that lets developers run containerized applications on Cloudflare’s infrastructure, alongside Cloudflare Workers.
Developers and companies building applications on Cloudflare typically use it, including those running backend services, processing jobs, and code execution environments.
Check out Salman Hoseini’s Ledge Detection and Navigation System for Unity, designed for minimal setup, handling complex geometry, and supporting IK-driven animation.
Available here.
It’s designed to work with minimal setup, handle complex geometry, and support IK-driven animation workflows.
A former Pixar FX Technical Director explains how his Multi-Layered RBD Simulation workflow lets Houdini artists add fractures and secondary detail while preserving approved destruction motion.
Full interview and research.
Destruction simulations can become extremely expensive to revise once their overall movement has been approved. Changing the fracture pattern or behavior of one area may alter piece indexing, constraints, collisions, and the motion of surrounding geometry, forcing artists to recalculate work that a director already liked. Former Pixar FX Technical Director Jae Jun Yi developed a layered Houdini workflow intended to make those increasingly specific production notes easier to address.
Presented at SIGGRAPH 2026, Multi-Layered RBD (Rigid Body Dynamics) Simulation begins with a relatively simple base simulation focused on composition and large-scale motion. Artists can then select individual pieces manually or through conditions such as size, velocity, impact strength, and collision events. Those pieces are fractured again, inherit the approved movement of their parents, and transition into a new dynamic simulation only when an artist-defined trigger is reached.
In this interview, Yi explains how identifiers, transform attributes, activation states, constraints, and collision adjustments maintain physical continuity between layers. He also discusses the workflow’s art-direction controls, its limitations for highly interconnected or interactive destruction, and his preference for extending Houdini’s familiar RBD toolset rather than creating a specialized system that other artists would struggle to understand.
Most industrial AI pilots are booked as if they are the start of a multi-year platform deal. In reality, most never become one.
RAND and MIT’s 2026 analysis puts the failure rate at 80.3%. Multiple studies converge on 70–88% of AI pilots never reaching production.
The bigger financial risk is the scale-up cost. Moving a successful pilot into production typically costs 250–400% more than the pilot itself. A $100,000 pilot can need $300,000–$800,000 more to go live.
Most buyers do not reserve that budget going in. When the real number appears after the fact, the project dies for lack of an approved line item — not for lack of results.
Vendors whose revenue holds up past year one tend to price the pilot and the scale-up as separate milestones, and tie part of the scale fee to actual production usage.
Pilot bookings are not a leading indicator of platform revenue. Production conversion is.
Last week, Sen. Bernie Sanders (I-Vt.) and Rep. Greg Casar (D-TX) introduced the “Ban Artificial Superintelligence Act,” a sweeping new bill that envisions comprehensive government control over AI and advanced computation. The measure contains some of the most radical interventions ever proposed in any piece of proposed legislation in American history. If enacted, it would have extremely destructive and dangerous consequences for innovation, competition, economic growth, global competitiveness, national security, and freedom of speech.
The bill’s provisions notably include up to 20-year jail sentences for certain violations, an indeterminate “pause” on AI development, and the creation of a new cabinet-level “Department of Artificial Intelligence” tasked with monitoring and controlling the technology. The bill also directs the administration to pursue international agreements with other countries regarding safety standards around AI, potentially implying restrictions on countries that continue to develop these systems outside of international frameworks.
Sanders has previously floated other proposals including the government taking a 50 percent stake in frontier AI companies, and the creation of an AI sovereign wealth fund the proceeds from which would be used to ensure that “economic benefits generated by AI are used to improve the lives of all of us.” Previous R Street essays have noted how these measures, if implemented, would not only stifle innovation and expose the federal government to potentially significant contingent liabilities, but also usher in unprecedented and dangerous control over this cutting-edge technology.
Meridian Space has split from kinetic launch technology developer SpinLaunch as it seeks more funding to develop flat satellites it says could launch up to 250 at a time on a Falcon 9, enough for global broadband coverage.
Key idea: take *expert* human writing and learn rubrics that find the gap between experts and models. Train with those rubrics.
We train with RL-XAR (RL with eXpert Aligned Rubrics) & see large performance gains on writing scientific paper sections, Pulitzer prize novel continuations and high quality Wikipedia pages.
First: The failure of standard LLM Judgements 💀
On paper writing tasks, strong judges (GPT-5.6 or Opus-4.8) think current ‘slop’ models are better than humans on selected high quality papers (using either pairwise, or using standard rubrics).
Our method can learn rubrics where the human is considered better by the grader (right in fig) – the key to training.
