标签：#c

Alibaba allegedly used 25,000 accounts to mine Claude over 28.8 million exchanges.

At the right point of the orbit and stellar cycle, the star's chromosphere brightens.

Polestar won't be allowed to sell its electric vehicles model year 2027 and beyond in the US after the federal government denied the company's request for authorization under a new rule banning vehicles with software from China. In a press release, the company says the decision to retreat from the US follows a recent decision […]

Did Full Self-Driving (Supervised), Tesla’s driver assistance feature, play a role in a woman’s death?

Base Power is skipping the PJM's troubled interconnection queue by placing its batteries at people's homes, offering backup services in exchange.

Giftlink: https://www.bloomberg.com/news/articles/2026-06-25/apple-to-... also https://www.msn.com/en-ca/lifestyle/shopping/apple-to-skip-h...

Despite ChatGPT's commanding market lead, consumers who pay for AI have been increasingly choosing Anthropic's Claude, data shows.

In the recent Springer/Meteor email, it says: The deadline for the upload of the camera-ready manuscripts and source files is 30 June. This is a hard deadline and will not be extended. However, in the same email, the Meteor submission line for my paper says: submission due: June 27, 2026 A previous email from the ECCV Program Chairs also stated that the camera-ready deadline had been extended to 30.06 AoE and that this deadline is final. Does anyone know whether June 27 is just an internal/default Meteor due date, or whether it is the actual deadline for uploading in Meteor? Since the email says there is only one upload and the first upload is final, I want to avoid uploading too early if June 30 is the correct deadline. this is really confusing. submitted by /u/National-Resident244 [link] [留言]

What if there was a new programming language where the meaning of each token was so dense (or perhaps so specific) that an LLM could write robust code with fewer tokens and faster inference? Assuming there’s enough training data, do you think something like this allow an LLM to write better code faster? Rationale: 1) It would allow for faster inference. Fewer tokens required to do the same thing in Python = finish faster. 2) It would allow for more information in a 1M context window. Whatever you could fit in 1M tokens of Python, you could do 100x that in this theoretical language. 3) It would effectively remove the “noise” from human readable language (semi-colons, curly braces for example) which I would think would make the LLMs coding ability stronger. I could be wrong about this of course. submitted by /u/Spongebubs [link] [留言]

Token-based billing is causing my company to reevaluate small language models. I came across this paper that shows SLM supervised fine-tuning on traces from orchestration of frontier models can be nearly as performant and much cheaper. Has any tried this in the real world? submitted by /u/ThirdWaveCat [link] [留言]

Cellebrite claims the hardware predates current sanctions and was used without its consent.

Earlier this month, a German court ruled that Google is liable for its AI search summaries. Rejecting defenses like “users can check for themselves,” and that they generally know “that information generated with AI should not be blindly trusted,” the court held that the AI’s summaries are reflections of the company and “above all an expression of Google’s business activities.” This is the latest skirmish in a decades-old battle over internet publishing. Historically, there were two different types of information distributors: carriers and publishers. A phone company is a carrier. It’ll transmit whatever you say, even discussions about committing a crime. Words are words, and the phone company does not know—nor is it liable for—the words you choose to speak. A newspaper, on the other hand, is a publisher. It decides the words it publishes, and what quotes to include in its articles. If those words or quotes are defamatory or otherwise illegal, it’s liable...

General Intuition has raised $320 million to scale AI trained on millions of hours of gameplay, betting action data can help AI develop something closer to human intuition.