I know. I have NodeBB as a backup.

I imagine that was part of it, but I doubt it’s the actual main reason. More of a post justification.

Or if you just ignore federal courts, which seems to be the current fashion.

Rclone can do file mounts as well as sync.

A lot of the answers here are short or quippy. So, here’s a more detailed take. LLMs don’t “know” how good a source is. They are word association machines. They are very good at that. When you use something like Perplexity, an external API feeds information from the search queries into the LLM, and then it summarizes that text in (hopefully) a coherent way. There are ways to reduce hallucination rate and check factualness of sources, e.g. by comparing the generated text against authoritative information. But how much of that is employed by Perplexity et al I have no idea.

I feel like this article is exactly the type of thing it’s criticizing.

Can you link the feeds?

The problem is that while LLMs can translate, it’s still machine translation and isn’t always accurate. It’s also not going to just be for that. It’ll be applying “AI” to everything that looks like it might vaguely fit, and it’ll stifle productivity.

Is the code available somewhere?

Well when Roosevelt was elected 4 times, it was actually legal back then. And he’s the reason why the 2 term limit amendment exists. But of course, that requires actually following the law, so…

Because of the porn or AI? 🙃

This is probably one of the best actual uses for something like generative AI. With enough data, they should be able to vectorize and translate dolphin language, assuming there is one.

1 scenario tested is better than 0 tested.

This guy would fit in well at my previous job where the founder discouraged writing unit tests because “there are too many scenarios to test.”

Like, wtf…

That was entirely the point unfortunately.

Lol, there are smaller versions of Deepseek-r1. These aren’t the “real” Deepseek model, but they are distilled from other foundation models (Qwen2.5 and Llama3 in this case).

For the 671b parameter file, the medium-quality version weighs in at 404 GB. That means you need 404 GB of RAM/VRAM just to load the thing. Then you need preferably ALL of that in VRAM (i.e. GPU memory) to get it to generate anything fast.

For comparison, I have 16 GB of VRAM and 64 GB of RAM on my desktop. If I run the 70b parameter version of Llama3 at Q4 quant (medium quality-ish), it’s a 40 GB file. It’ll run, but mostly on the CPU. It generates ~0.85 tokens per second. So a good response will take 10-30 minutes. Which is fine if you have time to wait, but not if you want an immediate response. If I had two beefy GPUs with 24 GB VRAM each, that’d be 48 total GB and I could run the whole model in VRAM and it’d be very fast.

They’re probably referring to the 671b parameter version of deepseek. You can indeed self host it. But unless you’ve got a server rack full of data center class GPUs, you’ll probably set your house on fire before it generates a single token.

If you want a fully open source model, I recommend Qwen 2.5 or maybe deepseek v2. There’s also OLmo2, but I haven’t really tested it.

Mistral small 24b also just came out and is Apache licensed. That is something I’m testing now.

Most open/local models require a fraction of the resources of chatgpt. But they are usually not AS good in a general sense. But they often are good enough, and can sometimes surpass ChatGPT in specific domains.

It’s enough to run quantized versions of the distilled r1 model based on Qwen and Llama 3. Don’t know how fast it’ll run though.

Don’t know about “always.” In recent years, like the past 10 years, definitely. But I remember a time when Nvidia was the only reasonable recommendation for a graphics card on Linux, because Radeon was so bad. This was before Wayland, and probably even before AMD bought ATI. And it was certainly long before the amdgpu drivers existed.