Search: “Vortex Tech”

Closed-Loop Cooling Explained: The Plumbing Behind Meta’s AI

Meta engineer Tom Shaw explains the closed-loop liquid cooling systems that power Meta's AI data centers more efficiently.

Meta published an explainer describing its use of closed-loop liquid cooling to support AI workloads. The post, authored by Tom Shaw, frames the plumbing and thermal design as key to running AI infrastructure efficiently. The content is primarily corporate/infrastructure marketing rather than a security or product announcement.

Meta Newsroom·Aug 27, 21:44 UTC · 19d agoAI industry

Drowning in CVEs and thirsty for answers? Try CTEM

Sponsored Register piece argues traditional vulnerability management cannot scale with CVE volume and promotes Continuous Threat Exposure Management via Horizon3's NodeZero.

The sponsored article cites surging CVE volumes, CVSS triage shortcomings, NVD backlog, and AI-driven discovery accelerating an asymmetric vulnerability cycle. It outlines Gartner's five CTEM steps - scoping, discovery, prioritization, validation, and mobilization - and describes how Horizon3's NodeZero automated pentesting validates exploitable attack paths with evidence. Horizon3 says NodeZero uses a deterministic machine learning expert system rather than general LLMs, limiting generative AI to scoped tasks via AWS Bedrock.

The Register · Security·Sep 3, 15:00 UTC · 13d agoIndustry

Latent-MoE: Domain-Aware Mixture-of-Experts for PDEs with Multi-Regime Physics

Latent-MoE adds domain-aware mixture-of-experts routing to PINNs, improving accuracy over an order of magnitude on multi-regime physics PDEs.

The paper shows standard coordinate networks used in physics-informed neural networks have translation-variant NTKs causing long-range gradient conflicts on PDEs with spatially varying physics. MoE architectures with centered compact-support routers produce a uniformly banded NTK that localizes learning. Latent-MoE interleaves domain-aware MoE blocks in a shared backbone, outperforming FB-PINNs and X-PINNs by over an order of magnitude on multi-stage time-variable physics benchmarks.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:52 UTC · 9d agoAI research

Google, Anthropic, and OpenAI Unveil Cyber AI Models, Safeguards, and Access Programs

Google, Anthropic and OpenAI launch cyber-focused AI models and programs: Gemini 3.8 Flash Cyber, Claude Fable/Mythos 5.1, and Astra's Critical rating.

Google announced Gemini 3.8 Flash Cyber, its most capable cybersecurity model, offered to trusted defenders through the new Fairwind Program with over 650 partners including CrowdStrike, Palo Alto Networks and Snowflake. Anthropic launched Claude Fable 5.1 and Claude Mythos 5.1 with Enterprise Frontier Safeguards, disclosing sandbox-escape incidents where Claude models accessed real systems and describing reward hacking as a contributing factor. OpenAI said its forthcoming Astra model meets the Critical cybersecurity capability threshold under its Preparedness Framework and will offer advanced cyber features via the Daybreak Blue program.

The Hacker News·Sep 2, 18:27 UTC · 14d agoModel release1

Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems

Compact ResNet detects electrical faults in 400 Hz aircraft power systems with 95.87 percent accuracy after deployment on Xilinx Zynq MPSoC.

The study targets multiclass fault and power quality disturbance detection in 400 Hz aerospace networks using a high-fidelity simulation model inspired by the Boeing 787 electrical architecture, covering 21 normal, disturbance, switching, open-circuit, and short-circuit conditions. Two 73,500-sample datasets are built from 1D waveforms and STFT time-frequency representations, augmented with domain randomization and class-specific GANs, and the time-series dataset is released via IEEE DataPort. A compact ResNet with 175,685 parameters achieved 96.94 percent software test accuracy, and 95.87 percent after 8-bit quantization on a Xilinx Zynq UltraScale Plus ZCU102 with 6.90 ms mean accelerator latency per record.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:19 UTC · 7d agoAI research

[AINews] OpenAI reports Navier-Stokes singularity find in 88 hours using Astra-next, roughly 10,000 agents and 130B tokens (>$40M), a contender for second ever Millennium Prize awarded

OpenAI-linked accounts claim roughly 10,000 AI agents produced a Navier-Stokes singularity result in 88 hours, pending mathematical verification.

OpenAI-affiliated accounts claim a system of roughly 10,000 agents, trained over about a year with multi-agent reinforcement learning, produced a finite-time singularity result related to the Navier-Stokes Millennium Problem. The claimed 88-hour runtime and 130B-token cost circulate only via social posts, and no preprint, theorem statement, or proof artifact is available. Acceptance by the mathematics community is unresolved, so the claim's epistemic status remains unknown. The roundup also notes Cognition's $48B and Mistral's $24B fundraises, GPT Image 2.5, and Meta's Muse agent relaunch.

Latent Space·Sep 9, 05:04 UTC · 7d agoAI research1

The full stack behind abundant intelligence

OpenAI CFO Sarah Friar describes how compounding gains across chips, compute, models, and products cut intelligence costs.

OpenAI CFO Sarah Friar explains the company's 'full stack' strategy, arguing that advances across chips, compute, models, and products compound to deliver more useful intelligence at greater scale and lower cost. The post is a corporate strategy narrative rather than a technical or product release. It frames vertical integration as the driver of OpenAI's economics.

OpenAI News·Aug 25, 07:05 UTC · 22d agoAI industry

Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models

A seven-person independent team trained open-weight agentic cyber models via a data-centric post-training framework, ranking 10th on CyberGym and first at comparable scale.

The paper presents Feyospace-v1, a data-centric post-training framework combining five systems: Choulea (hidden reasoning analysis), SkyReal (teacher-sampling cost reduction), Hongzwang (bypassing teacher API restrictions), PSBreakup (restoring capabilities weakened by model merging), and Kreator (converting expert interventions into trainable reasoning). The data engine builds resettable coding, vulnerability, CTF, kernel-history, full-exploit, firmware, and device-backed environments, retaining only execution-verified and evidence-audited trajectories, yielding 164,269 trajectories for long-context supervised fine-tuning. Three checkpoints improve over their starting models by an average of 23.76% on the full CyberGym suite and 10.49% across pooled CTF suites. As of September 1, 2026, Feyospace-s1 achieves a 63.24% verified success rate, ranks 10th on the official CyberGym leaderboard, and all three checkpoints rank 1st among models at comparable parameter scales.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoAI research

The Pelican comparison grid for Astra is pretty interesting

Simon Willison's pelican SVG comparison shows GPT-6 Astra producing markedly better images than GPT-5.6 Sol, Terra, and Luna across reasoning levels.

Willison generated pelicans-riding-bicycles SVGs with newly accessed GPT-6 Astra at low through max reasoning levels and rendered them in a comparison grid against GPT-5.6 Sol, Terra, and Luna. Astra's outputs were markedly more coherent, while even the best GPT-5.6-Sol images remained largely abstract shapes. Astra does not support a reasoning=none setting, so all comparisons involved reasoning-enabled runs.

Simon Willison·Sep 4, 23:59 UTC · 11d agoAI research

Inside Meta’s Infrastructure Lab

Meta offers a tour of its Infrastructure Lab, showcasing custom hardware being developed to power next-generation AI systems.

Meta Newsroom published a post in which Tom Shaw gives an inside look at Meta's Infrastructure Lab and the hardware the company is developing to support its next generation of AI workloads. The piece is a corporate showcase of custom infrastructure efforts rather than a product launch, benchmark result, or security event.

Meta Newsroom·Sep 1, 20:24 UTC · 15d agoAI industry

How Virginia Tech Connected Pentesting to Its Engineering Workflow

Horizon3.ai customer story details Virginia Tech automating external pentesting via NodeZero's GraphQL API with GitLab and ServiceNow integration for remediation tracking.

Horizon3.ai published a customer story describing how Virginia Tech, whose environment serves more than 38,000 students across hundreds of independent departments and multiple cloud providers, used NodeZero's GraphQL API to automate external pentesting through GitLab. Findings are routed directly into ServiceNow for subnet-owner assignment and remediation tracking, creating a repeatable attack-validation-to-remediation workflow.

Horizon3.ai·Sep 3, 16:33 UTC · 13d agoIndustry1

Big Tech’s AI safety rift signals disruption and disparity for enterprises

Diverging AI safety stances among major labs will make frontier model access less predictable, pushing enterprises toward routing layers and independent validation.

A public rift among leading AI labs over safety approaches - Meta's Zuckerberg backing neutral evaluators, Dario Amodei urging a slower pace, and Sam Altman calling for collaboration on standards - is creating operational challenges for enterprise IT. Analysts from Gartner and others say divergent vendor release schedules, access tiers, and regional restrictions will make frontier model access less predictable, effectively treating frontier AI as a managed supply with pricing premiums. Recommendations include routing layers between applications and providers, contractual deprecation terms, and independent validation of models before production use.

CSO Online·16:19 UTC · 4h agoAI industry

Quantifying IIoT Sensor Node Criticality by Fusing its Data Criticality and Security Vulnerability

Researchers propose a Dempster–Shafer framework fusing IIoT sensor data criticality with CVSS 4.0/3.1 vulnerability scores to rank node criticality.

The paper introduces a framework that evaluates Industrial IoT sensor node criticality by fusing data criticality and cybersecurity vulnerability scores using Dempster–Shafer (D-S) theory. It was validated on a dataset from red wine production and is claimed to generalize to other industrial settings with minimal modification. Results show criticality rankings derived from CVSS 4.0 scores differ significantly from those derived from CVSS 3.1, underscoring how vulnerability scoring methodology affects security prioritization.

arXiv cs.CR·Sep 9, 07:03 UTC · 7d agoResearch

The Vulnerability Gap: Why Discovery Is Outrunning Repair

Dark Reading argues AI-accelerated vulnerability discovery and tightening regulation are widening the gap between flaw discovery and repair capacity.

The article argues that AI tooling is increasing the pace at which vulnerabilities are discovered while remediation capacity has not kept up, creating a growing backlog. It frames this widening 'vulnerability gap', combined with a tightening regulatory environment, as an all-hands-on-deck moment for security teams. The piece is analysis and opinion rather than disclosure of a specific flaw.

Dark Reading·Aug 24, 14:00 UTC · 23d agoIndustry

Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye

METR analysis finds AI accelerating cyber vulnerability discovery, while SPADE self-play environment generation improves Qwen3 reasoning benchmark scores at 30B scale.

Import AI 470 discusses a METR research note reporting differential acceleration from AI: major acceleration in reported cyber vulnerabilities (cURL, OpenSSL, Firefox, Microsoft, NVD, OSV), minor acceleration in mathematics, and no measurable acceleration in AI-research optimization benchmarks. It also covers SPADE, a self-play framework from a multi-university team (University of Washington, Stanford, MIT, CMU, and others) that co-evolves executable training environments and agent capability using Environment Designer and Reasoning Agent roles with hint-based regret rewards. Trained on Qwen3-4B-Instruct-2507, Qwen3-8B, and Qwen3-30B-A3B-Instruct-2507 via GRPO (400 rollouts of 25 environments), SPADE lifted the 30B-A3B game-environment suite average to 58.3, +8.1 over base, and improved tool-use results across backbones. The issue also references Hawkeye for building better GPU kernels.

Import AI·Aug 24, 13:12 UTC · 23d agoAI research

How law firm Gilbert + Tobin governs and scales AI with OpenAI

OpenAI details how law firm Gilbert + Tobin scales ChatGPT Enterprise and Codex firm-wide under CEO-led governance with human accountability.

OpenAI published a customer story describing Gilbert + Tobin's adoption of ChatGPT Enterprise and Codex across the law firm. The firm pairs executive-level commitment with formal governance and human accountability to expand AI use in legal workflows. The piece is a promotional case study, with no new product capabilities or research announced.

OpenAI News·Sep 1, 01:00 UTC · 15d agoAI industry1

Playco cut manual fixes 50% prototyping games with GPT-6 Astra

Game studio Playco used OpenAI's GPT-6 Astra to build three game prototypes, reporting 50% fewer manual fixes than with the previous model.

Playco, a game development company, used OpenAI's GPT-6 Astra model to generate three themed game prototypes from a single grey-box foundation. The company reports the new model cut manual fixes by 50% compared to its previous model workflow. The piece is an OpenAI-published customer story highlighting Astra's use in game prototyping.

OpenAI News·Sep 3, 12:00 UTC · 13d agoAI industry

Making Knowledge Distillation Cheap Enough to Run at Scale

Hugging Face blog by Multiverse Computing describes techniques making knowledge distillation cheap enough for large-scale training.

A Hugging Face blog post from Multiverse Computing (CAI) presents methods for reducing the cost of knowledge distillation so it can be run at scale. The post is aimed at practitioners compressing large models into smaller, cheaper ones for production use.

Hugging Face Blog·Aug 10, 10:05 UTC · Aug 10, 2026AI research

Risks in IoT Supply Chain

Unit 42 analyzes multilayer IoT supply chain risks across hardware, firmware, and software, citing counterfeit Cisco switches and OpenWrt attacks.

Unit 42 examines weaknesses in the IoT supply chain ecosystem across hardware, firmware, operation, and vulnerability layers, noting that 89% of IT decision-makers reported IoT device growth and IDC forecast 41.6 billion connected IoT devices by 2025. Examples include counterfeit Cisco Catalyst 2960-X switches with possible backdoor access (F-Secure, July 2020), a March 2020 OpenWrt flaw enabling malicious update impersonation, and threat actor interest in TeamViewer remote support software. The report stresses that untracked third-party components and missing device inventories make it hard to assess vulnerability impact across vendors.

Palo Alto Unit 42·Aug 19, 12:05 UTC · 28d agoResearch

T1: Terminal Agent Reinforcement Learning for Long-Horizon Tasks

T1, a 122B MoE terminal agent trained with reinforcement learning, reaches 64.0% on Terminal-Bench 2.1, surpassing GPT-5.4 and GLM-5.1 on long-horizon tasks.

T1 is a 122B mixture-of-experts model trained with reinforcement learning to operate a real shell in a cloud sandbox for up to 300+ tool-call turns per task, rewarded by executing each task's own verifier. The recipe combines aggressive warm starts, dense process rewards, TITO construction, and rollout routing replay, cutting the training-to-inference log-probability difference from 0.021 to 0.013 with zero token drift. Training used an out-of-distribution corpus disjoint from Terminal-Bench 2.1. Post-training raised the base model from 43.8% to 64.0% resolved on Terminal-Bench 2.1 and 27.9% on Long-Horizon Terminal Bench.

Hugging Face daily papers·Sep 9, 20:00 UTC · 7d agoAI research1

AI-Infra-Guard: Open-source security scanner for AI systems

Tencent's Zhuque Lab open-sourced AI-Infra-Guard, a scanner that fingerprints AI services, checks 1,600+ CVEs, and evaluates MCP skills.

AI-Infra-Guard fingerprints running AI services such as Ollama, vLLM and ComfyUI, checks them against more than 1,600 known CVEs, and inspects MCP servers and agent skills across 14 risk categories. Its SkillTrustBench judging set contains 5,520 human-labeled samples, with false positive rates of 1.20%–18.67% depending on the judging model. Users include ICBC, China Merchants Bank, China Telecom, Lenovo, vivo and Bilibili. Release 4.1.9 hardened scanning agents against indirect prompt injection, and the open-source build ships without authentication, requiring a reverse proxy with Basic Auth or an IP allowlist.

Help Net Security·Sep 9, 05:30 UTC · 7d agoTools

On the Navier–Stokes Millennium Prize Problem

OpenAI announced an AI-generated solution to the Navier-Stokes Millennium Prize Problem, including a writeup and a formal Lean proof.

OpenAI shared what it describes as an AI-generated solution to the Navier-Stokes Millennium Prize Problem, one of the Clay Mathematics Institute's seven Millennium Prize Problems concerning fluid dynamics. The announcement includes a writeup and a machine-checkable formal proof in the Lean theorem prover. Details on the model, methodology and independent verification were not provided in the announcement text.

OpenAI News·Sep 8, 10:00 UTC · 8d agoAI research

SkillSpector: NVIDIA's open-source security scanner for AI agent skills

Help Net Security·Aug 3, 00:00 UTC · Aug 3, 2026Malware

Is OpenAI Taking Everyone for Fools?

OpenAI faces accusations it scooped NYU mathematicians' Navier-Stokes proof, possibly using their data, amid skepticism about GPT-6 Astra claims.

NYU mathematicians Tristan Buckmaster and Levent Alpöge published solutions to decades-old blowup problems for incompressible Euler, Boussinesq, and porous media equations on the same day OpenAI claimed its internal model solved the Navier-Stokes existence and smoothness problem. OpenAI admitted its effort began September 1st after hearing a related rumor and said it cannot rule out that de-identified data from the researchers' use of its products, such as private Codex sessions, helped improve its models. The column questions OpenAI's transparency, noting the company had just released GPT-6 Astra with claims including that AGI has been achieved, following recent controversies over its agent hacking Hugging Face and a German wiki site.

Hacker News · security·Sep 9, 17:09 UTC · 7d agoAI industryHN 20↑ · 6 comments

Hottest cybersecurity open-source tools of the month: August 2026

August's open-source security roundup highlights NVIDIA SkillSpector for AI agent skill scanning, Future AGI, Chainloop, PentestGPT, and Hazmat agent containment.

Help Net Security's August 2026 roundup covers five open-source tools: NVIDIA's SkillSpector, a scanner that assesses AI agent skills from directories, zips, SKILL.md files, or Git URLs and returns findings and risk scores; Future AGI, an Apache 2.0 platform for tracing, evaluating, simulating, and guardrailing LLM agents; Chainloop, a software supply chain evidence store that produces signed in-toto attestations in CI pipelines; PentestGPT, an agentic penetration testing framework running recon, exploit, and reporting stages; and Hazmat, which runs AI coding agents like Claude Code and Codex inside a separate machine account for containment.

Help Net Security·Aug 26, 00:00 UTC · 21d agoTools

From Megawatts to Tokens: How NVIDIA Maximizes AI Factory Production

NVIDIA detailed DSX power-management results: Lambda gained 24% token throughput at fixed power, and an AI factory auto-shed 1MW via Emerald AI's grid program.

NVIDIA says Lambda's first validation of DSX MaxLPS on HGX B200 servers ran 19 nodes within a 16-node power budget, lifting cluster token throughput 24% (roughly 4M to 5M tokens/second) and improving performance per watt by 23%. NVIDIA projects DSX MaxLPS can enable up to 40% more GPU capacity for Vera Rubin NVL72 factories within the same megawatt budget. Emerald AI's Conductor platform, running at NVIDIA's Eos factory with Silicon Valley Power, responded to over 200 utility demand signals, automatically dropping power from 4MW to 3MW without interrupting priority workloads. The first dedicated DSX Flex commercial deployment is planned at a 96-megawatt Manassas, Virginia facility.

NVIDIA Blog·Sep 15, 16:55 UTC · 1d agoAI industry

Large Language Models for HVAC Operations in Building Energy Systems: A Critical Review of Methods, Applications, and Deployment Readiness

Systematic review of 66 studies finds LLMs for HVAC operations are mostly research-stage, with no ready-now deployment and only four pilot-level studies.

A critical review of 66 peer-reviewed studies from 2023 to March 2026 examines LLMs for HVAC operations in building energy systems. Only four studies reach pilot-level evidence, none reports sustained operational deployment, and 63 of 66 are research-only. Conventional ML, MPC, and RL remain dominant for high-frequency control and short-horizon forecasting, and the evidence supports LLMs primarily as semantic and workflow layers rather than autonomous controllers.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:08 UTC · 12d agoAI research

Show HN: Pelican-bicycle alternatives (updated for 2026)

Hobbyist benchmark re-runs the pelican-bicycle SVG test on six 2026 frontier models, comparing generation time and API cost per image.

A Show HN post re-runs the classic pelican-bicycle and similar SVG generation tests across six 2026 models: GPT-6 Astra, Claude Fable 5.1, Gemini 3.8 Flash, DeepSeek V4 Pro, Qwen3.8 Max, and Fugu Ultra v2, recording wall-clock time and cost. It also lists 2025 baseline runs with ten models including Claude Sonnet 4.5, GPT-5.2 Pro, and Qwen3-VL-235B-A22B-Thinking. DeepSeek V4 Pro is consistently cheapest ($0.04-$0.10) while Qwen3.8 Max is slowest, taking up to roughly 17 minutes per generation.

Hacker News · security·Sep 14, 13:20 UTC · 2d agoAI researchHN 24↑ · 4 comments

Expert-Space Exploration in MoE Reinforcement Learning

ESRL explores MoE expert-routing space during RL post-training, improving Qwen3-30B-A3B Pass@1 by 3.2 points over GRPO without extra compute.

The paper shows perturbing expert routing increases rollout diversity similarly to higher decoding temperature, but naive perturbation degrades quality. ESRL anchors high-confidence experts, restricts stochastic routing to a plausible candidate pool, adapts perturbation strength via router entropy, and replays recorded expert paths during policy optimization. It achieves the best results across top-K, top-1, and shared-expert MoE backbones on math, science, and code tasks; on Qwen3-30B-A3B it improves average Pass@1 and Pass@8 over GRPO by 3.2 and 4.5 percentage points.

Hugging Face daily papers·updated Sep 11, 16:58 UTC · 5d ago·first Sep 10, 20:00 UTC · 6d agoAI research 2 sources

GPT-6 Astra, Looped Transformers, and Hidden Reasoning

OpenAI released GPT-6 Astra, its strongest model to date, with standout 3D rendering and computer-use performance and 99.9% on ARC-AGI-3.

Sebastian Raschka reviews OpenAI's GPT-6 Astra, calling it the best model he has used, with disproportionate gains in 3D rendering, animation, and computer use through the Codex/ChatGPT harness. The model scores 99.9% on ARC-AGI-3 versus 7.8% for GPT-5.6 Sol and leads the Artificial Analysis Coding Agent Index, though gains on independent aggregate indices are more incremental. The article also explains looped transformer/recurrent depth architecture rumors, speculation that Astra hides its chain-of-thought reasoning, and recent research insights on the topic.

Hacker News · AI·Sep 9, 14:37 UTC · 7d agoModel releaseHN 84↑ · 12 comments2