Using BYOC management reduces the costs significantly. The big cost in analytic SaaS offerings is generally compute, which vendors mark up significantly. (They keep margins low on storage.)
Disclosure: My company Altinity offers BYOC management of ClickHouse.
Right, and as a consequence lots of very important software (orders of magnitude more important than tailwind) does not have full-time employees. Good for them if they can manage it, but it's a gravy train.
Given how hard it is to build any form of financially sustainable model around an open source project I think we should actively celebrate anyone who manages to build a model that works, not dismiss it as a "gravy train".
I really just think they got lucky, I don't see anything to celebrate. It's like celebrating one of your friends winning the lottery - good for them, but it's not really an indication the system is working well at all.
This was after they laid everyone off, so it was just Adam and Steve left. An they surpassed $2M a year in sponsorships a couple of months after that. They were not struggling for cash at all.
A lot of open source that we used to use as dependencies is trivial. Sometimes you only need 10% of the library.
No one is talking about kernel.
Also, changing license does not prevent you from forking the source code before the change. There are no new libraries with kernel level effort that use uncommon license.
From what pros are saying after matches, especially in classical chess, it's almost always preparation and metagaming the opponent vs finding good moves.
It's a lot of mind games involved, going for specific openings, repeating same moves as last match, doing a poor by surprising, move etc.
Many GMs said that to win a match you have to play non-ideal moves many times, otherwise it will likely end in a draw.
Maybe because it's like a swiss army knife for data work, regardless of whether you need it for OLTP or OLAP workloads. Having different SQL dialects is a bit annoying, but the base is the same more or less, so switching doesn't come at too big of a cost.
As a huge duckdb fan, I'd love to see chDB to get proper windows support - that would make it real competition (having WASM coverage is already a big step) which would be good for the space as a whole.
Is the google infra stable enough right now? At the start of the year, the flash model was unusable for a whole month via gemini CLI. They could not fix it for a whole month and I was a paid customer.
I feel the same, there are still lots of people who are interested in learning internals, but they are hard to reach. Even such people don't use Google anymore, so they won't find your content.
> The whole point of his model is to optimize for a very specific benchmark.
But benchmaxxing is what we generally try to avoid for training, as there is no point really for it. We used to call it "overfitting", now you're saying this person does it intentionally? Why?
There are plenty of applications where a machine learning system needs to optimize for a very limited data set that is still intractable by linear logic systems of reasonable scale and complexity. It’s interesting, because he is using the legos of LLMs to build highly specialized machine learning systems, which is a very pragmatic approach. Obviously a lot of other ways to achieve similar goals, but it’s cool to see someone back porting the modern tools towards older style optimizations.
Also, the complexity of the task he is using occupies an interesting middle ground of ultra high dimensionality (for a “simple” problem) while being limited in width to a narrow set of solves- a space where one would be tempted to imagine you would need a much more capable system.
Overfitting, as well as the specific instances I've seen of the word benchmaxxing, involve knowing the answers and training to those answers. That did not happen here. The model is limited in scope, which means it's not being scored on generic intelligence, but neither is it defective and terrible at solving new problems inside its scope, like you get with overfitting.
And physical damage is lot more expensive to repair in vast majority of use cases. Have fun when your humanoid window washing robot punches through your window. Or any other such cases.
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