Meta has restored Muse sharing with new, privacy‑safe URLs after fixing a flaw that exposed public links.

A new software‑only jailbreak for the Switch 2, built with AI assistance and an old game, lets modders run custom content but remains complex and tethered.

Eiliko’s maker Energize Lab says it will hire an independent security firm and take immediate steps after The Verge exposed serious privacy flaws in its AI robot toys.

Bill Gates warns that the next two decades of AI development will be turbulent and demand proactive safety and policy measures.

AI shopping agents are emerging but face retailer blocks, technical hiccups, and ethical concerns that limit their usefulness.

Google DeepMind and partners released a 10‑meter global Forest Typology map that distinguishes six forest types and tracks their changes from 2017‑2025, enabling more precise conservation and compliance with regulations.

Nirva’s $33 M raise funds a lightweight, fashion‑forward pendant that passively listens only to its wearer and turns speech into personal insights.

Continued pre‑training on larger, more diverse game data improves embodied agents’ performance in familiar worlds and offers modest transfer to related virtual environments, but distant worlds still expose generalization gaps.

SpecEvo coordinates cheap and frontier language models to cut discovery costs while keeping performance high.

Agent4Sci shows that current frontier coding agents can modestly improve scientific ML benchmarks, but their gains rely on engineering tricks rather than genuine research insight.

Successive Scale Racing (SSR) adaptively selects evidence combinations to improve agentic harness evolution as task pools grow, outperforming prior methods while cutting validation cost.

The survey reviews over a hundred studies on deploying reinforcement‑learning algorithms on FPGAs and integrated circuits, highlighting architectural approaches, performance trade‑offs, and open research challenges.

SimHack uses a lightweight simulator trained on a few small RL runs to forecast when reward hacking will emerge, helping researchers pick safer training recipes before costly RL experiments.

Embedding operational context into enterprise AI transforms decision-making, boosts efficiency, and mitigates costly errors.

AI‑driven researchers uncovered two decades‑old PHP vulnerabilities, prompting urgent patches across millions of sites.

AI inference generates massive, often overlooked storage costs that grow faster than compute, demanding scalable, tiered storage strategies.

Granite Timeseries Ensemble combines four pre‑trained IBM models using simple probabilistic or adaptive IQR weighting to deliver robust, out‑of‑the‑box forecasts without retraining.

U.S. Hunger uses Amazon Quick to give its lean leadership team instant, shared access to data and definitions, turning raw information into collaborative decision‑making across finance, operations, and research.

The SNTO framework uses neural networks and GPU‑aware domain partitioning to deliver scalable, high‑resolution stress‑constrained topology optimization.

KVLearn uses learned reuse prediction and cost‑aware scoring to decide which KV‑cache blocks to keep in disaggregated LLM serving, cutting latency and network traffic dramatically.

TF-IG combines lightweight temporal and spectral branches with an interaction‑gated fusion to deliver sub‑2 BPM heart‑rate errors using only ~85 K parameters.

Use AWS PrivateLink and VPC connections to keep Amazon Quick traffic private, compliant, and low‑latency between corporate networks and AWS.

The cyber‑risk debate over open‑weight AI models is stuck in a false binary, and sensible policy must acknowledge trade‑offs, global perspectives, and the nuanced role of both open and closed models.

Commerce.com and Cloudflare use edge‑injected WebMCP scripts to give AI agents reliable, token‑efficient access to storefront tools without requiring merchants to rewrite their sites.