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* BRIN indexes are great for append only with an incremental value (a 50MB instead of 100GB index in timeseries data in my case)

* If your disks are ssd and scsi in different volumes, adapt the random cost of io in the config to let the planner now

* if you have RAID controller and a Battery-Backed Write Cache (BBWC), you can disable Linux filesystem write barriers. removing excessive fsync from the mouth of the psql demigod in SoCaL ~linux 2015, Bruce Momjian IIRC

* monitor disk usage, in backups pipe to gz, never to disk

* counter-intuitevely modern hardware may have an io bottleneck and plenty of cpu, so try Filesystems like ZFS using Zstandard (zstd), for boost in your Transactions per second

* if possible, schema multitenancy instead of database multitenancy, instagram talked about this decades ago

* indexes index functions results too, precompute those fields and partial indexes help a lot

* BM25 and FTS in pg are so good you probably don't need Elasticsearch and you will save a lot in de-sync between both of them

* you may be hacked in this brave new world post Mythos, thus, learn PITR to an external only write, no override medium like S3


your honor, openai means nothing, just ask elon

about the use of ANY, that's perfect for tracking changes on an audit table per field


yeah, I think what makes this post different was that AI did it. hopefully science will advance faster in the next decades with AI researchers helping


[flagged]


You honestly think this wouldn’t be upvoted equally or more if a Chinese model did it?


Yeah


There have been multiple posts on here with 800+ upvotes in just the last few weeks for GLM 5.2.

The idea that all this enthusiasm is for certain Silicon Valley billionaires and not from genuine interest in AI technology is a baffling take.


To be clear, I am not agreeing that people only upvote their favorite billionaires, just that if this particular thing was done by a Chinese model it would not have gotten the same attention


> a Chinese model it would not have gotten the same attention

Well, you would be wrong.


Interesting point I guess, much to consider


Consider actually searching for GLM or other top Chinese models and see how many upvotes they have been getting.

Your claim that Chinese models don’t get as much attention is easily proven false.


Im using silicon valley collequially to represent "tech", not as a google maps pin.


So what billionaire are people rooting for when a new GLM comes out?

How many people do you honestly think even know who owns or leads Z.ai?

I certainly don’t.


Hopefully. So far it seems to be doing more harm than good.


What harm? I think the major part of the US economy seems to be depending on AI.. without that, the economy seems bad..


I have used iridium before, IIRC I paid 1 usd per KB, PER KILOBYTE (!!!), to track some stratospheric globes we launched in like 2014


seems they charge almost usd2 per KB now. oh well.


may you please tell us how much effort goes into each type of task in those months?

where else do you think these techniques be applied?


We are a core team of about 10 researchers and developers working full time on work that applies to all of the scrolls. We also ahve 4 full time annotators that tend to work on one scroll at a time. The amount of time spent on any given scroll varies with how difficult and large it is.

There is an extremely large overlap between a lot of the work we do with medical imaging, CT scanning, XRay technology, and such. A lot of the ML models and frameworks we have used and adapted for our purposes originated in the medical field for things like cancer detection or segmenting different body parts.


wanted sovereignty, bought a Blackwell for usd12k, discovered a billing issue in some customer and explains that will cover the card

I don't follow how it supports the decision of buying the card, I would even say using online SOTA models would had caught it earlier without usd12k and monthly electricity being spent


Author here. Thanks for the question. I'll answer assuming this is a question you have for me.

As explained in the post - the 3090s were what were the test bed that proved the investment was worth it. Customer support, architecture reviews, telemetry to check license compliance. None of that could be done with online models. The amount of time we can spend going backwards and forth with enterprise customers over email can really amplify costs to our team. A few actual issues we found and fixed were listed on the linked blog post: https://www.openfaas.com/blog/painless-support-with-diag/

Having recovered revenue using it in an airgap, to preserve data agreements was more of a cherry on the cake. No need to worry about the investment, it's covered itself.

Hope that helps.


storing data over years takes money, so charging for it I can understand

but charging and knowing you don't have any data for this user is a big NO NO


Wow. Lede well buried. I thought I got the story, but after a while I started scanning over all the edge and missed that actual story. I really though we were complaining about spending $5 to retrieve data that had been stored for years at no cost.

Pretty seedy move. That, and what appears to be the dark pattern of prompting for payment first, then ultimately allowing export after refusal.


Agreed! I'd (sadly) pay more than $5 to recover some childhood memories that some services have deleted instead. Not $5/mo, though (I hate that part too).


for the love of the game, very refreshing good ol' coding


bro, what an anchor to the past that framework is


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