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Amazon is turning a corner with its launch providers, but ULA's Vulcan remains grounded.

Spotify's new features aren't AI slop for a change

submitted by /u/iximiuz [link] [留言]

The most memorable part of 007 First Light is something that's typically pretty boring: the tutorial. In many games, you're forced through a series of tedious lessons in how to play, presented in a way that feels disconnected from the story itself and at a plodding pace. But First Light does something different. Because the […]

Researchers say they’ve found a new way to extract lithium, a crucial metal used in the lithium-ion batteries that power electric vehicles and energy storage arrays. This new technique could be more environmentally friendly and cheaper than existing ones. The research was published today in Science, and a startup called Rock Zero is working to…

Iron-rich immune cells in the liver may act as sensors for magnetic fields, serving as an internal compass.

Are agents aging after deployment? : https://arxiv.org/abs/2605.26302 On a new longitudinal deployment benchmark, switching the Claude Code CLI agent from Sonnet 4.6 to Opus 4.7 dropped PyTest pass rate by ~15%. This (to me) is a counterintuitive-enough result to pay attention to. The authors built AgingBench , to measure how coding agents hold up over a long deployment, not just on a single task. On their S7 coding scenario, swapping the backbone model from Sonnet 4.6 to Opus 4.7, within the same Claude Code CLI harness, produced a 15% mean drop in PyTest pass rate across the deployment horizon. Their argument is that this is a longitudinal effect, not a raw-capability one. The benchmark stresses how an agent's memory state evolves over many sessions (compression, interference, revision, maintenance shocks), and a stronger base model doesn't automatically age better under a given memory policy. In fact, memory policy alone drove a 4.5x spread in agent half-life across scenarios, which is larger than any model swap they tested. All to say: "newer model, just swap it in" may not be a safe upgrade strategy for long-lived agents. More details and a runnable benchmark: https://agingbench.github.io Does this reflect your experience with long-lived agentic deployments? submitted by /u/CategoryNormal149 [link] [留言]

Intel's Arc B390 integrated GPU has offered impressive performance in laptops.

The new Opus model comes with a tool called Dynamic Workflows, for coordinating swarms of subagents.

The launch of the Ojai minivan robotaxi comes after years of development and testing, but arrives amid a challenging time for Waymo.

Wall-OSS-0.5 is a new 4B VLA release from X Square Robot, built on a 3B VLM backbone with action experts in a Mixture-of-Transformers layout. What caught my eye is that the report evaluates the pretrained checkpoint on real robots before task-specific fine tuning, instead of only reporting downstream fine-tuned performance. The reported numbers are: zero shot on a 17-task real-robot suite, 4 tasks above 80 task progress, including a held-out deformable task (Rope Tightening, 82). After fine tuning on a 15-task suite, they report 60.5 average task progress, +17.5pp over pi0.5, and +26pp on the 10-task manipulation subset. They also report +21.8pp on embodied grounding while general VL ability stays stable. The method bits I am trying to sanity check are the gradient bridge and the optimizer claim. They argue that discrete action-token CE is the dominant gradient into the VLM backbone, while flow matching's contribution to backbone updates collapses to roughly 5 percent within a few thousand steps. The Vision-Aligned RVQ tokenizer is supposed to make those action tokens semantically grounded instead of just numerical compression. For continuous actions, they still use flow matching, but supervise in recovered action space rather than velocity space. They also include DMuon, a distributed Muon optimizer, with a pretty aggressive overhead reduction claim. Code: https://github.com/X-Square-Robot/wall-x . Hugging Face org: https://huggingface.co/x-square-robot . Project page: https://x2robot.com/oss#resources . Paper: https://x2robot.com/api/files/file/wall_oss_05.pdf The questions I had after reading it: if you have run an analogous gradient-bridge ablation in another VLA, did action-token CE dominate in the same way? For people already using Muon, does the DMuon overhead claim sound plausible? And has anyone seen RVQ-with-vision-alignment clearly beat FAST-style tokenization outside this paper? If anyone is already trying to reproduce this on real hardware, drop notes.

One leading privacy lawmaker said it was time to "start treating the adtech industry as a national security threat."

Next month's Tribeca Festival will include the premiere of an AI-generated film: Dreams of Violets. The 75-minute film is a fictional dramatization of the Iranian government's mass killing of protestors in January, with the people and images fully created by AI, as reported earlier by The Hollywood Reporter. Dreams of Violets cost $2,000 to make […]

Activision is planning to drop support for PS4 and Xbox One consoles in Call of Duty: Warzone later this year. Players will need to upgrade to a PS5 or Xbox Series S / X console to continue playing Call of Duty: Warzone once season 6 of Call of Duty: Black Ops 7 finishes later this […]

New features coming to YouTube could make it better for listening to podcasts, rolling out to Premium subscribers starting today on Android and coming later to iOS. A new "on-the-go mode" shifts YouTube into an audio-first layout, with larger, simplified playback buttons, a still image in place of the video, and a timeline showing video […]

Nearly 10 years ago I reviewed my favorite Surface device. Microsoft hand-delivered its Surface Studio all-in-one PC to me, and I was hooked from the moment I switched it on. It had a beautiful floating touchscreen that you could push all the way down into a drawing board mode, making it unlike anything I had […]

Krafton has been trying to weasel out of making paying the Subnautica 2 developer a bonus for a while now.

Did you know that a 35-billion-parameter model can generate tokens at the same compute cost as a 4B model? That single fact made me abandon a multi-model agent architecture I'd spent a weekend building. But I had to run the benchmarks first to understand why. Here's the full breakdown, with commands, numbers, and the architectural reason it all falls apart on shared-memory hardware. The Discovery That Changed Everything I'd been running qwen3.6:35b on my Minisforum UM790Pro for weeks as my daily coding assistant. 17.8 tokens/second -- genuinely usable for interactive work. But I kept wondering: could I run a lightweight sidecar model alongside it for quick classification and tool-calling in an agent pipeline? Before I even started benchmarking, I dug into what qwen3.6:35b actually is under the hood. It's a Mixture of Experts model: 256 total experts with only 8 activated per token. The architecture also incorporates SSM (State Space Model) components alongside traditional attention -- Mamba-style layers that handle certain sequence patterns more efficiently than pure transformers. The math hit me: 8 out of 256 experts means each token only touches roughly 4-5B parameters worth of compute. The model carries 36 billion parameters of knowledge , but its per-token cost is comparable to a small dense model. I was planning to run a separate 4B model for "fast tasks" next to a model that already operates at 4B-class speed. But I had to prove it with numbers. Hardware and Ollama Setup The UM790Pro specs that matter for this experiment: CPU: AMD Ryzen 9 7940HS (Zen 4, 8C/16T) iGPU: AMD Radeon 780M (12 RDNA 3 compute units) RAM: 96 GB DDR5-5600 (~80 GB/s bandwidth) GPU memory pool: 2 GB dedicated VRAM + 46 GB GTT = 48 GB GPU-accessible That 48 GB GPU pool sounds enormous until you realize it's carved from the same DDR5 that the CPU also uses. There is no separate GDDR6 bus. Everything -- CPU inference, GPU inference, KV caches, OS operations -- flows through one 80 GB/s pipe.

Europe, Australia, and Asia can still get it while the getting's good.

One poisoned extension, one package install, one CI workflow. Any of them can now be the first domino. That is the uncomfortable lesson from the latest Shai-Hulud activity and GitHub’s recently confirmed internal-repository breach. The scary part is not only the number of affected packages, tokens, or repositories. Counts move fast. The scarier part is where the attacker code ran: inside the trusted developer and CI path. The modern supply chain is not just “the dependencies we ship to production.” It is your IDE, your package manager, your GitHub Actions runner, your cache keys, your OIDC flow, your local gh auth, your AI coding tool config, and the cloud account that quietly pays the bill when something goes sideways. What happened, briefly CISA described the original Shai-Hulud wave as a self-replicating npm worm that compromised more than 500 packages and targeted GitHub personal access tokens plus AWS, GCP, and Azure keys. GitHub later said it removed 500+ compromised packages and began pushing npm toward shorter-lived credentials, 2FA enforcement, and trusted publishing. The later waves got more CI-aware. Instead of only stealing npm tokens from maintainers, they looked for credentials inside build environments, abused publishing workflows, and used the build system itself as distribution. Microsoft’s May 2026 reporting on the @antv ecosystem described a “Mini Shai-Hulud” style campaign that targeted GitHub Actions environments and stole GitHub, AWS, Vault, npm, Kubernetes, and 1Password secrets. Microsoft said GitHub removed 640 malicious packages and invalidated 61,274 npm granular access tokens with write permissions and 2FA bypass. Then GitHub confirmed an incident involving a compromised employee device and a poisoned third-party VS Code extension. GitHub said the attacker’s claim of roughly 3,800 internal repositories was “directionally consistent” with its investigation, while also saying its current assessment was exfiltration of GitHub-internal reposi