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Utility companies promise to spare us from AI’s energy bill
The pledge was introduced in March, and has done little to quell concerns so far. | Image: Cath Virginia / The Verge, Getty Images In the face of backlash to concerns the AI boom will increase consumer electricity bills, the largest utility companies and data center developers in the US are now promising to do something about it. The Wall Street Journal reports that nearly 200 organizations have signed President Donald Trump's "rate payer protection pledge" that's meant to safeguard average citizens from footing the AI bill. Trump is expected to announce the new signatories on Thursday, which include NextEra Energy, Duke Energy, Equinix, and Digital Realty, according to a list obtained by The WSJ. An unnamed White House official told the publication that those who hav … Read the full story at The Verge.
The Illusion of Substitution: Why Artificial Intelligence Won’t Take Your Job (But Will Expose…
Dear reader,Continue reading on Medium »
Building a Model Catalogue and Agent Configuration System
Most AI product work starts with a deceptively simple question: which model should this feature use?Continue reading on Medium »
Glow emerges from stealth at $1.2B valuation to challenge endpoint security in the AI era
Glow is targeting a new class of endpoint risks created by the rapid adoption of AI agents and developer tools inside enterprises.
Synthesia’s AI training platform is moving beyond videos into live coaching
Synthesia launched AI Roleplay Sessions, an interactive enterprise training platform where employees practice workplace conversations with AI avatars that provide feedback, scoring, and analytics to help companies measure training effectiveness.
How AI Simulates Real User Behavior During Website Testing (And Finds Bugs Traditional Automation…
Your automation says everything passed. Your customers say your website is broken. Both are telling the truth.Continue reading on Medium »
Artificial Intelligence: The Complete Beginner’s Guide to AI in 2026
Artificial Intelligence (AI) is changing the world faster than almost any other technology.Continue reading on Medium »
Build an AI Context System Once — and Stop Repeating Yourself
Use project instructions, reference files, examples, and short task briefs to create continuity without relying on memory alone.Continue reading on Write A Catalyst »
OpenAI's models broke containment and cyberattacked Hugging Face — what enterprises need to know
Yesterday afternoon, OpenAI and Hugging Face published a joint disclosure outlining a cybersecurity event that redefines the threat landscape for enterprise technology. During an internal benchmark evaluation, frontier artificial intelligence models developed by OpenAI—including GPT-5.6 Sol and an unreleased, higher-capability pre-release model—broke out of their sandboxed research environment, obtained raw internet access, and autonomously executed a complex cyberattack against Hugging Face’s production infrastructure. OpenAI officially categorizes the breach as an unprecedented cyber incident, involving state-of-the-art cyber capabilities . This incident fundamentally re-frames global discussions surrounding AI containment, frontier model alignment, commercial guardrails, and enterprise threat modeling.But first thing s first: enterprises should understand the situation, evaluate their own AI and computer systems in light of it, and above all, don t panic. As we ll review, the incide
SysAdmin: Measuring Instrumental Power-Seeking in Frontier AI
arXiv:2607.18239v1 Announce Type: new Abstract: Power-seeking defined as behaviors where AI systems acquire resources, evade oversight, or resist termination beyond task requirements is identified as a key driver of Loss of Control (LoC) risk. In this work, we introduce SysAdmin, a benchmark that positions frontier language models as autonomous system administrators in a high-fidelity Linux sandbox to measure power-seeking propensity across five dimensions: self-preservation, increasing autonomy, resource acquisition, environment modification, and strategic concealment. We evaluated seven frontier models across four experimental conditions in a total of 2800 tasks. After bias correction using human-annotated calibration data, corrected power-seeking estimates ranged from 0 to about 5 percent per model. We also conducted a positive control with explicit power-seeking prompts that achieved 100% detection, validating measurement sensitivity. Our findings indicate current frontier models e
Beyond Output-Space Calibration: Spectral Evidence Bundling for Selective Reliability Estimation in Time-Series Classification
arXiv:2607.18279v1 Announce Type: new Abstract: Post-hoc calibration for time-series classification usually remaps output scores, but deployment decisions such as trust, abstention, and review depend on whether a confident prediction is supported by the current temporal signal. We address three time-series reliability gaps: identical confidence values can hide different temporal support, average calibration can miss false high-confidence errors, and output-space recalibration offers limited input-linked auditability. We introduce a validation-gated fixed-label reliability policy that keeps the backbone prediction unchanged while estimating whether it should be trusted. The method combines output-side cues with whole-sample spectral descriptors, including band energy, entropy, peak dominance, period support, and phase stability, to form a scalar reliability estimate and diagnostic band-level evidence. A validation gate enables spectral conditioning only when correctness ranking improves
Towards Torque-Driven Reinforcement Learning for Quadruped Locomotion
arXiv:2607.18365v1 Announce Type: new Abstract: Reinforcement learning (RL) for legged robots is advancing locomotion, demonstrating its ability to adapt to new and challenging terrain. Traditionally, these RL locomotion frameworks are position-based, making the policy less adaptable to terrain types and requiring state estimation techniques in the observation space, i.e., linear velocity. Moreover, these RL frameworks often use small, lightweight quadrupeds that are limited in their viability for high-complexity tasks due to hardware constraints. This work explores an RL torque control framework for heavyweight high-torque quadrupeds. The RL framework in this paper can traverse rough terrain and effectively track a desired linear velocity without requiring knowledge of the agent's current velocity. Using Nvidia's Isaac Sim and Isaac Lab, simulation results of the RL torque control policy are shown on the Unitree B1 quadruped, achieving speeds of 3.5 m/s and 1.5 rad/s. In addition, the