Tootfinder

BYD has developed a technology to eject the battery upon a severe crash in order to prevent fires or explosions.
Tesla has developed a technology to suspend the self driving mode milliseconds before crash so they legally aren't liable when the car catches fire or explodes.

People who don't mind driverless cars will find all sorts out reason to brush off this kind of news:
https://flipboard.social/@TechDesk/117135628617386440
It was moving slowly already to be safe, it did brake before the collision, the injuries were minimal, the child is at fault, etc.
In the end, a lot of people will shrug and say: human drivers also make mistakes like this, so what's the big deal if a robot does? Maybe the robot even has a better safety record on average than humans. You can't ask for perfection!
Here are three related reasons why you shouldn't buy these arguments:
1. The crash statistics for the "average human driver" include (and are probably significantly driven by) impaired human drivers, like drunk drivers or people on their phones. When driverless car companies put "as good as the average human driver" as the goal, they're saying that they're okay with their systems being just as bad as drunk or distracted drivers sometimes. That's not the right goal at all! We should demand that driving robots perform at least as well as non-distracted human drivers, which is significantly *better* than the average human driver when it comes to things like crashes.
2. Mistakes made by a robotic system are fundamentally different in nature from mistakes made by humans, because the robot mistakes ate *systematic* across all robots in similar-enough situations. If a human looking at their phone slows down too late and hits a child, that's tragic, but it doesn't mean that *every* human driver put into the same situation would make the same mistake. In fact, many of them probably wouldn't be looking at their phone and thus would avoid the accident, *even given the exact same situation in which to react.* In contrast, *every Waymo taxi running the same software* will make exactly the same mistake in that situation. In other words, this kind of event will definitely repeat whenever a similar-enough situation arises around a Waymo taxi. That's why news like this should be scary, even though the accident was a minor one.
3. Some might claim that it's good that a software upgrade could potentially fix the issue that caused this accident. It's sort of true that there's an upside there (although any behavior updates can cause errors in other situations). But the question we should be asking is: why wasn't this already fixed before the vehicle was approved for testing on real roads with real children and people in harm's way? Why wasn't there a stimulation scenario for this exact kind of situation, so that the system already knew what to do? Why should it take a child getting injured for a fix to be developed v(if one in fact will be)? In theory, of course, it's possible that this was a truly exceptional situation where even an alert human couldn't have done better. I'm sure what's what the company is telling the investigators. It's also possible that the designers are following the Silicon Valley motto of "move fast and break things", which reaches an entirely new level of frightfulness when the thing that's moving fast is a car and the thing being broken is a child. "I'll just deploy it now and as test cases when I see what really breaks in production" is an attitude I've been guilty of, but then I make videogames, not deadly robots.

Four sources of pleasure associated with the experience of groove
Music, in both perception and production, is a uniquely powerful source of pleasure, engaging cognitive, bodily, psychological, and social processes. This paper focuses on groove, an everyday, effortless form of sensorimotor musical experience. While previous studies have solely examined groove as pleasurable urge to move, this paper develops a theoretical framework for understanding groove as a multidimensional source ……

Frontiers | Four sources of pleasure associated with the experience of groove
Human behaviour is largely driven by the desire to maximize pleasure. Music, in both perception and production, is a uniquely powerful source of pleasure, en...

Spec-Driven Hardware Evolution via Executable Contract Refinement and Proof-Guided RTL Update
Shibo Zhao, Yang Zhang, Mengxia Tao, Baoqi Zhang, Kezhi Li, Qiang Xu, Binwu Zhu, Hao Yan, Min Li
https://arxiv.org/abs/2608.12684 https://arxiv.org/pdf/2608.12684 https://arxiv.org/html/2608.12684
arXiv:2608.12684v1 Announce Type: new
Abstract: Hardware development is inherently evolutionary: major revisions typically begin by changing intended behavior and then updating a previously validated implementation, rather than regenerating RTL from scratch. Yet most recent LLM-based hardware research still frames the task primarily as prompt-to-RTL generation, offering limited support for semantic version evolution of trusted legacy designs. We present spec-driven hardware evolution, a contract-centered formulation for RTL version iteration. Instead of treating a new feature request as a direct prompt for RTL generation, we refine it into a reviewed executable contract for the next version. This contract specifies what must hold at the externally visible transactional level through a behavior-level reference together with explicit observation and checking semantics, while leaving how the change is realized in RTL to the evolution process. Based on this formulation, we organize hardware evolution into four stages: Specify, Plan, Implement, and Validate. After contract approval, the remaining stages proceed automatically: Plan derives cross-version semantic deltas and localizes affected RTL regions, aided by mutation-based semantic probing; Implement and Validate then perform legacy-aware RTL update under proof-guided checking and iterative repair. We evaluate the framework on a controlled version-evolution case study of a representative TPU datapath block under data-format changes. The results support the feasibility of contract-driven hardware evolution and demonstrate that the proposed backend workflow can effectively drive validated legacy RTL toward next-version functional convergence under a reviewed executable contract. An anonymous artifact for reproducibility is available at https://anonymous.4open.science/r/SDHE-3A6C.
toXiv_bot_toot