TimesFM: a pretrained time-series foundation model developed by Google
https://github.com/google-research/timesfm

TLDR: They trained a model on lots of real world time series data and now you can use it to make predictions on any time series. One shot, not fitting anything. The performance in benchmarks looks impressive.

https://stacker.news/items/1574324

lol https://github.com/SwissBitcoinPay/app/commit/1a23cb2e811f584639bdfe01d78765705bdc2398

🇨🇭 Swiss Bitcoin Pay potentially hacked
https://x.com/SwissBitcoinPay/status/2099473448162488618

> A malicious user has likely gained access to Swiss Bitcoin Pay’s internal systems. As a precaution, we are temporarily shutting down our servers while we investigate and secure our infrastructure.

https://stacker.news/items/1571579

Would it be better if Bitcoin was "completely confidential"?

Let's skip the "shitcoins" debate. Would it be better if Bitcoin was "completely confidential" ™️?

Assuming it was like that from the beginning and that we had certainty of no inflation bugs being possible. Lots of hypotheticals, but to center the debate around full confidentiality vs. the state.

The idea comes from https://stacker.news/items/1571256/r/m0wer. The thought behind it is whether "privacy" coins are a bad idea because they become a clear target for the state or actually the opposite because they "force" people to opt-out. Maybe there are many other considerations.

The question is, if you had a magic wand and could make Bitcoin have been completely confidential from the beginning without bugs, would you?

https://stacker.news/items/1571269

Zcash: EU AMLR prohibits anonymisation-enhancing coin accounts
https://www.tradingview.com/news/coinmarketcal:27d9af694094b:0-zcash-eu-amlr-prohibits-anonymisation-enhancing-coin-accounts-10-jul-2027/

Controversial(?) opinion: "privacy" coins are a bad idea because they become a clear target for the state. Or maybe the opposite because that would mean "forcing" people to opt-out?

https://stacker.news/items/1571256

A Severe Misalignment of AI in Mathematics
https://terrytao.wordpress.com/2026/09/11/a-severe-misalignment-of-ai-in-mathematics/

TLDR: Tao and 24 other Fields Medalists warn that AI companies are optimizing for “solving” famous math problems, while mathematics is really about developing understanding, ideas, and new methods. Rapidly mass-producing solutions risks destroying the human process that turns problems into lasting knowledge, while creating serious attribution and plagiarism issues. They’re not against AI in math; they argue it should accelerate understanding rather than turn research into a benchmark race.

https://stacker.news/items/1570423

The AI Language We Can't Read: Neuralese - YouTube
https://www.youtube.com/watch?v=iuHddnIzKRA

First, we gave models a scratchpad: instead of forcing a one-shot answer, let them spend more tokens reasoning through a problem. In a sense, you get more capability out of the same model by giving it more serial computation.

That reasoning is also useful because humans can read it. You can inspect how the model reached an answer, spot mistakes, and potentially debug or monitor it.

But there’s an obvious incentive to make that reasoning cheaper. Compress it, shorten it, remove redundant words. The problem is that if you keep optimizing for efficiency, the reasoning can drift into shorthand or “Neuralese” that still works for the model but becomes gibberish to us.

So there’s a tradeoff: more efficient reasoning vs. preserving one of the few windows we have into how the model reached its conclusion.

https://stacker.news/items/1569287