61–70 of 106 posts
They sure are productive. What are we going to see tomorrow? DeepSeek OS or something?
To be fair they’ve been working on this since 2019 for HFT. So it’s not like they just whipped this up.
I think the difference between deepseek and OpenAI/Anthropic is one of the difference between practitioners and academics. Ofcourse there is world class talent at OpenAI. But there are also alot of "I went to Harvard and want to work in AI", and those types of people just simply dont have the technical exposure to even think of building something like this.
Someone should write a blog post about the prestige/effectiveness negative feedback loop. This is also the Achilles heel of top tier SV VCs including YC.
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enlighten us
my theory is that their own DeepSeek writes the code for them, so they are highly productive.
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It’s not. When you are a high frequency trader and you’ve mastered RDMA, everything around you looks slow. You are thinking in terms of 20 nanoseconds intervals, while everyone around still thinks that serving a query under a millisecond is fast.
Huh? What kind of RDMA has a completion latency of 20 nanoseconds? It's more like 5 microseconds. I agree that a lot of "modern" storage stack is way too slow though, tried to find a replication-first object storage for crazy-fast random read in small number of objects last year and found none.
Why this number - this is because it’s roughly the time it takes to read 64 bytes from L3 cache. And NICs tend to be able to push data into L3 (or equivalents).
Current state of the art - look up nanoPU, from Stanford. Wire-to-wire under 100ns is not impossible, but this would normally assume pre-cooked packet, selected from a number of packets (which is not an unusual scenario in HFT).
I think the difference between deepseek and OpenAI/Anthropic is one of the difference between practitioners and academics. Ofcourse there is world class talent at OpenAI. But there are also alot of "I went to Harvard and want to work in AI", and those types of people just simply dont have the technical exposure to even think of building something like this.
Someone should write a blog post about the prestige/effectiveness negative feedback loop. This is also the Achilles heel of top tier SV VCs including YC.
Can someone convince me this isn't NIH syndrome? Why would you use this instead of SeaweedFS, Ceph, or MinIO?
I think the difference between deepseek and OpenAI/Anthropic is one of the difference between practitioners and academics. Ofcourse there is world class talent at OpenAI. But there are also alot of "I went to Harvard and want to work in AI", and those types of people just simply dont have the technical exposure to even think of building something like this.
Have we in the valley companies lost touch?
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That would be terrifying in itself if true because for this type of work you really need the best of the best. But I doubt this is the case here. LLMs as we know them today are not quite yet there for this type of work.
Do you, though? Need the best of the best?
Why do you think this would be controversial? This isn't an every day work.