与「AImodel」相关的搜索结果

女性のお客さんを乗せた後はドラレコの映像を確認するようにしている。 なぜだかわかるかい？ After giving a ride to a female passenger, I always check the dashcam footage afterward. Do you know why? #AIgirl# #AImodel# #AI美女# #妄想工房# #MOSOStudio#

Hello guys, tonight I watched a 50 minute podcast with Albert Castellana from GenLayer on The Future of Crypto Compliance, and one minute in it is still stuck in my head. Code is free now, execution is becoming free, so what still has value? According to Albert, it’s judgment. AI already decides what we see on Twitter, whether we get hired, whether we get a loan or insurance, and right now we’re just trusting a few big AI labs with that power Main points: - His previous project lost access to around $150M after keys were lost, and the courts turned out to be too slow and expensive - GenLayer is building a small digital court onchain for decisions that aren’t just yes or no - Five random validators, each with its own AI model, vote on the outcome - Disagree? Appeal, and the validator set doubles each time, up to 1,500 - The first decision takes minutes and costs around 50 cents - It doesn’t replace real courts, it handles small disputes nobody would take to court anyway

I have a major result to share. I’ve found conclusive forensic evidence that the top 4 Chinese AI labs—DeepSeek, Moonshot, MiniMax and Ant Group—have built their newest models on NVIDIA’s most advanced Blackwell chips, which are barred from export to China. This sounds insane, but I discovered that almost every AI model carries a hidden signature of the GPU that trained it. When a model is first set up, the starting values of its weights are laid out in a pattern that depends on the exact GPU. Each combination of GPU and seed number produces a unique pattern—a sort of fingerprint of the hardware used to create the model. Traces of that pattern survive training into the published model. Again, this evidence isn’t based on any insider information or leaking. It’s based on a forensic analysis of the model weights released by the companies themselves. I searched more than six trillion combinations of chip layout and seed number and matched them to open-weights models. In doing so, I found conclusive evidence that six Chinese AI models—including DeepSeek-V4-Pro, Kimi K3, and MiniMax-M3—were initialized on NVIDIA Blackwell systems. Four of the models match Grace Blackwell, the GB200 and GB300 parts in NVIDIA’s flagship liquid-cooled rack-scale systems. I then verified these results on real hardware. Running unmodified PyTorch with the seeds I’d recovered, a Blackwell B300 reproduced DeepSeek-V4-Flash’s untouched starting weights bit for bit, and a rented GB200 reproduced Kimi K3’s to a correlation of 0.999995. A Hopper H100 reproduced none of DeepSeek-V4-Flash’s, and changing the seed by one broke the match. (DeepSeek’s seed is 953049583 by the way.) The method also was verified to match 24 known open-weights models to the hardware that developers said they used. It is exceedingly unlikely that these results are a coincidence. Across six trillion combinations, the best accidental match should reach only about 8 sigma (i.e., eight standard deviations above what random chance would produce). The real matches range from 49 to 1,385 sigma, and even the weakest single test hits 20. These were data-center-scale training runs. With some analysis of release schedules and model details, I estimate that the largest, Kimi K3 and DeepSeek-V4-Pro, used 2,000 to 3,000 Blackwell GPUs each. Clusters of this size cost on the order of $150 to $200 million each to buy, and across these labs alone would add up to more than half a billion dollars of export-controlled GPUs, not counting experimental compute or otherwise. It’s quite likely that we see multi-billion dollar expenditure on export-controlled GPUs. I am highly confident in this result, and it has been independently verified by senior researchers at OpenAI and Anthropic. You can read the full report at There is much more I’ve identified about these models through other forms of checkpoint forensics like this, and will have more to share soon.

Introducing Token Exchange, a neutral, transparent and trusted marketplace connecting AI model service providers with customers. It gives customers one place to compare models side by side, review clear pricing and performance data, and start using them. For quality providers, it opens a new channel to reach more customers. Before listing, we check provider credentials and compare model fingerprints to help detect model swapping and inflated specs. After listing, we conduct regular spot checks and ongoing monitoring, and maintain a public credit record for each provider. One month in, the platform has more than 100 model resources, averaging over 50M calls and 700B tokens a day.

Fable 5.2 is the MOST dangerous AI model right now. It gives you AGI-adjacent reasoning. That can discover new profitable trading strategies for you 24/7. If you set it up correctly, you gain super powers.

Claude Fable 5.2 is the most insane AI model in history... 99% of people will never touch it or burn money on it: "Too expensive", "Not for me" Use it right, and it becomes your second BRAIN, for a fraction of the price In this article, I show you how:

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution

Grok @bot will be insanely powerful if it’s completely model agnostic and the @SpaceXAI simply hyper-focuses on creating the greatest orchestrator AI of all time. That orchestrator AI will know what model is best at what at the best possible cost and fastest speed, and based on your prompt, Grok Bot will automatically use the best model(s). And over time SpaceXAI can hyper-optimize their own models to be extremely good at executing tasks in Grok Bot. It’s getting WAY too hard for humans to know which AI model is best at what already, especially at what cost and at what speed. The only way to do this properly is with another AI that automatically does all of this for you. And since SpaceX is gonna own so much compute, in theory, those other models will likely be served from their own data centers either on the ground or in space! $SPCX