Nebius sounds nice. I'm currently with runpod.io (there's also vast.ai). If anyone has more European providers, I'd be interested, too. Especially pre-paid or some good control over spending, since I'm just tinkering for fun and not doing it as a job. And I don't want to risk getting a huge bill at the end of the month.
hendrik
joined 7 months ago
Sehr gut. Ich hab mir grad Bitwarden installiert und meine ersten beiden Passkeys erstellt.
I think it's not really rooted in facts. AI is an unsubstantiated hype and the stock market is a bubble. People seemed to have been under the impression, that OpenAI was going to invest several trillions(!) of dollars into Nvidia chips. To me, that always seemed a bit unrealistic. But that's what inflated the Nvidia stock. And now it turns out, to everyones' surprise, that OpenAI isn't the only company who can do AI. And that AI is making advancements and is getting better and more efficient all the time... So that trillion dollar bubble collapses.
To me, that's just silly. AI making progress was the very reason for those people to invest in it. Plus it's not like there is another company manufacturing the chips... Deepseek used Nvidia chips. So IMO they proved they're even better than people previously thought and there is room for improvement... But seems to me the stock market is set on doing it one specific and ineffective way, so it theoretically would need more hardware to do AI.
I think it'll turn out the opposite. The better AI gets, the more it'll get adopted. And that'll lead to more sales, not less. And if Nvidia hardware turned out to be better than we thought, it just proves they're ahead of their competition. So even more reason to invest in them. But the stock market sometimes just does silly things and isn't focused on long term goals.
Can't you feed that back into the same model? I believe most agentic pipelines just use a regular LLM to assess and review the answers from the previous step. At least that's what I've seen in these CoT examples. I believe training a model on rationality tests would be quite hard, as this requires understanding the reasoning, context, having the domain specific knowledge available... Wouldn't that require a very smart LLM? Or just the original one (R1) since that was trained on... well... reasoning? I'd just run the same R1 as "distillation" and tell it to come up with critique and give a final rating of the previous idea in machine redable format (JSON). After that you can feed it back again and have the LLM decide on two promising ideas to keep and follow. That'd implement the tree search. Though I'd argue this isn't Monte Carlo.
Is this a new thing? I thought that's how companies like Google and Meta operate for the last 10 years or so, minus a few poor people in underprivileged countries who have to sort the really bad stuff...
Btw, neither is this model. And it even has a worse license, and doesn't seem to compare at all, since this is a fine tune of Llama3.1 on maths questions?! and deepseek v3 is from the grounds up, has a MoE... And OpenAIs models are are different story altogether. Plus these benchmarks aren't even far off... Or say anything (like most AI banchmarks).
Does't seem too hard to me. I personally didn't. And it's kind of hard to track what happeded, with all the articles on DeepSeek.
I'd just take some prompt/agent framework like Langchain. That has Chain of Thought prompting built in for quite some time already. And then connect it to R1. That shoud do it. Maybe the thinking blocks need to be handled differently, idk.
The Chinese government mandates (political) censorship. So as as Chinese company/startup Deepseek did that. It doesn't seem very elaborate, though.
Here is a long article about it, which got mentioned in the technology communities: https://www.promptfoo.dev/blog/deepseek-censorship/
Kdeconnect. Alternatively NextCloud or sending an email to myself.
Seems they're introducing lots of errors lately... First Pixelfed, now entire Linux...
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Super Idee, direkt Nägel mit Köpfen machen...