orderone_ai

7 ms·

1.
by orderone_ai 7mo ago
Don't those have like 8GB of VRAM? Ain't no way you're running something that produces GPT-4 like results on that.
2.
by orderone_ai 7mo ago
Do you have figures supporting that? Because so far everything I've seen points to current inference subscriptions being wildly unprofitable. It may be a bit dated, but I haven't seen any new reports on unit economics coming close
3.
by orderone_ai 7mo ago
It absolutely does matter. LLMs still have to consumer context and process complexity. The more LoC, the more complexity, the more errors you have and the higher your LLM bills. That's even in the AI maximalist, vibe-code only use case
4.
by orderone_ai 10mo ago
It really depends on how it scales. If it can scale to LLM sizes via this training method (Which is a big if), then it could mean fundamentally overturning the transformer architecture and replacing it with RNNs in the most optimistic case.
5.
by orderone_ai 11mo ago
We're actually in a great place to reverse aging already with off the shelf stuff you can get from the grey market. We have countless interventions that show improvements in aging-related metrics in humans that show verified longevity
6.
by orderone_ai 1y ago
Ah, that's the beauty of it! It's not an LLM. It's a new class of model: A DSRU / Direct Semantic Reasoning Unit. It's a vec2vec architecture - it takes in 3 bge-large embeddings of the task, the input data, and the
7.
by orderone_ai 1y ago
Thank you for your question! Because I'm not sure exactly what you're looking for when you say 'compares to' -- whether accuracy, speed, or architecture -- I'll hit all 3, but sorry if it's a bit much. 1. Accur
8.
by orderone_ai 1y ago
Thank you for the question! I would say that ease of use and deployment is actually a good reason to have a single model. We don't train 20 LLMs for different purposes - we train one (or, I guess 3-4 in practice, each with their own br
9.
Show HN: A reasoning model that infers over whole tasks in 1ms in latent space(github.com)
3 points by orderone_ai 1y ago|6 comments
10.
by orderone_ai 1y ago
Or - crazy idea here - they're just full of it. I haven't seen an LLM stay on task anywhere near that long, like...ever. The only thing that works better left running overnight that has anything to do with ML, in my experience, is
11.
by orderone_ai 1y ago
Which quadrant is NOT concerning to you?
12.
by orderone_ai 1y ago
Overall, I agree - it would take far more sophisticated and deterministic or 'logical' AI better capable of tracking constraints, knowing what to check and double check, etc... Right now, AI is far too scattered to pull that off (
13.
by orderone_ai 1y ago
This is such a convenient tool for a casual user, and a great application of an LLM to a narrow task that probably couldn't be handled quite so easily everywhere. Also a great example of the emerging 'chat driven' UX trend, w
14.
by orderone_ai 1y ago
Man, that is truly fascinating. Do you have ideas on how to expand the study to capture broader analysis like that...?
15.
How much attention do you need, really? Experiments in O(1) latent reasoning(notion.so)
2 points by orderone_ai 1y ago|1 comments
16.
by orderone_ai 1y ago
Hello, fellow kids! I want to share what I've been working on the last few weeks: O(1) inference across whole tasks through direct vector transformation. A few facts upfront to give you an idea of how it goes: 1. Implemented as part of