An urgent notice from the Railway team blog.railway.com/p/security-…

🚀 DeepSeek-V4.1-Flash-1017 is here! Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API. 🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model. 1/4

you’ll get mad at me for saying this…but cloud gaming is so obviously more economically efficient than physical hardware I think it’s going to be the default soon. your home console / pc is idle 90%+ of the day. meanwhile, data centers targets what, 5%, maybe at worst 10% idle. every second a cloud gamer isn’t gaming, that hardware is being used for someone else, training, etc. I think there should be a new measurement, something like cost-per effective FLOP hour that takes into account the TCO + effective utilization. If a gamer spends $500 on a GPU, uses it for 3 years, but it’s only fully active ~5% of that period…the cost-per relative FLOP hour is crazy high! Meanwhile, a $50,000 datacenter GPU might have a *LOWER* cost-per FLOP hour just because the effective utilization is 90+%.

NEW: 🇩🇪 All tram service suspended in Leipzig, Germany due to the high temperatures, thei rails are melting.

I am down to play. I’ll pay realized SDH100RT index performance and receive compounded SOFR, with monthly resets/net settlement, on $1mm notional.

Interesting. (Founder of Z.ai, creator of GLM AI models.)

Our new paper lead by @vedanglad w/@AToliasLab "Letting the neural code speak..." arxiv.org/abs/2605.12485 We show how to get *monkey* visual neurons to TELL us in *human* language what images make them fire. We do this is an automated verifiable way at scale! How? 1) Build a digital twin of monkey visual areas that can accurately map visual inputs to neural activity. 2) Perform in-silico experiments on this twin to find many complex images that make a model neuron fire. 3) Use a vision-language model to describe these complex images. 4) Verification: use a language-conditioned diffusion model to generate new images, and check they make the monkey digital twin neurons fire a lot. To our knowledge, for the first time, we have a way to convert monkey vision to human language, and from *human* language to sample infinitely many images that make any given *monkey* visual neuron fire, all in an algorithmic fashion. For more exciting details, see @vedanglad's excellent thread!