Enterprise will pay anything if it's part of a contract bundle with the big guys, but individuals won't have to worry about cost increases. Chinese AI providers can easily undercut these services by offering 90% of the capability at a fraction of the cost (same goes for an increasing number of domestic hosts). Lots of software developers are getting by with $10/month plans from these guys. If they don't plateau, the best-case scenario for Anthropic and OpenAI is that the competition will always be a few months behind. There is no way for anyone to get far ahead when their output can automatically be distilled (using projects like dataclaw) to make cheap clones of their products.

It's not easy to tell AI music from something man made - it maybe sounds "fine" at first, but you will start to feel bad as you listen to it. It's an uncanny effect. Fortunately, I use Apple Music which is (so far) not subject to most of this problem.

There are 3 major problems with QuickShare: 1.) You need to have QuickShare. When you need to share something, the chances of a given person having your specific app installed (out of the thousands available) is basically zero. 2.) You need to use the app. There is no app for AirDrop; it's a system-wide service that works with virtually any datatype you encounter on an Apple device. You just click the "Share" button and then choose who to AirDrop stuff to. 3.) It can only share files. In many instances, AirDrop creates the data package on the fly - it can share URLs, contact info, locations, passkeys, notes, etc. File sharing apps generally can't do this because there are no files associated with those.

The idea of deleting a consumer's data is at odds with the requirements for CCPA opt-out. Anyone who submits an opt-out request needs to be on record that they don't want their data to be used for certain purposes. If a data broker is required to delete the consumer's data, how are they supposed to honor opt-out going forward as they get updated data that might include the identities again?

There are open models that can trade blows with big commercial ones (like DeepSeek and Qwen), but those are too large to run on notebooks today. The models that you can run on a laptop tend to be in the 120b - 20b range. They are not as great for general knowledge, but can excel at purpose-specific tasks. Take a look at MedGemma, or Qwen3 Coder Flash. Qwen Coder is super fast on a good laptop, and can beat the original GPT-4 at most coding tasks. Amazingly, these smaller models are often as good at the state-of-the-art models from 12-18 months ago.

Why are we singling Google out here? Sure, they run the Android project, but almost nobody actually buys a Google-branded phone. Surely Samsung or Xiaomi could offer a more open Android device if they wanted to.

Low carb diets are usually high fat, not protein. Maybe thatâ(TM)s why your friends had a tough time of it?

Language models ARE neural networks. What would make you doubt that?

The headline is not representative of what the survey says AT ALL, which is that current AI cannot be scaled up to AGI. Anybody with half a brain could have told you that more research is still needed. Even Sam Altman has said as much. Saying that current AI is a âoedead endâ is completely ignoring all of the demonstrably useful stuff it can do now.

I understand how it sucks for artists to see their work used in ways that they didn't intend, but I don't think there is much we should be doing about it. If the West starts imposing fines or fees for AI training, all that means is the best AI products will start coming out of China. They don't give a rip about IP laws, and would love to see their competitors hamstrung.

If you could be bothered to host DeepSeek yourself, there is no such censorship. R1 will happily talk about the issues regarding Taiwan and Tank Man. Of all people, Slashdotters should know to not rely on cloud services. https://x.com/fabianwilliams/s...

Those are great models, but they are not THE DeepSeek that all the buzz is about. They have more detail about them on GitHub: https://github.com/deepseek-ai...

There is only one version of DeepSeek R1. It has 671 billion parameters, and common 4-bit quantization requires about a half terabyte of fast memory to load it. If you squeeze it down to 1.5bpw, you can run it in just 200 GB of VRAM (with some loss of quality). The different âoeversionsâ you might be referring to are just ye old Llama and Qwen, which have been fine tuned with R1 data. Completely different architectures from DeepSeek.

You can run the DeepSeek R1 671B at home, as long as you have enough trash laying around with a total of 500GB VRAM. A handful of Mac Minis can do it. https://x.com/alexocheema/stat...