niltecedu

That was the first version they imrpvoed after that

I just dont go there at all because they added the weird copied of stackoverflow feature when copying code from there. Like even if I want to use its actively hostile, I need to paste it somewhere remove all of the extra things added and then copy properly

ffffffff.... damnit I love python but why is it so difficult like this. JIT would have been so perfect so the language, increasingly overtime with AI code coming through I am not sure not focusing on making the performance better is going to be better for longer term. Even though a lot of the other operations done in python are done faster in libraries I would still like faster startup times, or faster loops.

Yeah but podcast is low quality time; shows,movies are high quality, games are high quality high focus, music and podcast are lower on the rung. Its something you can have playing in the background and not pay attention.

Like the time I have for high quality stuff and low stuff are different and therefore different audiences. For Low quality stuff podcasts are fighting against tiktok

Not really, the time I set aside for shows, podcast, movies and games is all different. I am not going to give up watching slow horses to watch a podcast,

Some HPC Clusters, xgboost, read pretrained model from NFSRDMA Server, read data, parallelise over MPI with one process per model. And the latest Zen5 Epycs. I could do it all on just one of the 192 cores EPYCs but we like to parallelise it over multiple machines to get the extra speed and reliablity.

It wasn't that expensive, the main problem was the storage bottleneck which got fixed with just some LVM cache and gen5 ssds.

They are very dynamic, like too dynamic but we had really good predictors for it based on our existing data and past historical data.

Yep, tried them multiple times but too many tradeoffs and not matching rmse kills it for us. MAE isn't that impressive for us.

But why is the question, I run about 400k models every 30 minutes at work. I have tried all other approaches, but xgboost model per timeseries has never been beaten before. Even if I wasn't targetting accuracy, Xgboost loading a pretrained model and predicting with it and still faster.

I forecast 400k timeseries every 30minutes at work.

system operator and the transmission operator, system operator keep throwing a wrench into pre-made plans, transmission operator are a complete monopoly who bully their way around

I dont see why people arent mentioning edge in the comments? I think with e5 license and purview, its the same as what I am seeing mentioned in Firefox Enterprise?

Not unsurprising, excluding grid efficiency and industrial demand, its also just households. More and more places are getting batteries wherever possible, plug in solar is also avaialble. The amount of demand I don't forsee increasing at all unless people start getting air source heatpumps.

National Grid can mean NGED, NGET or NESO, god I love the UK. 2/3 of those are completely cuckoo regarding long term planning so not surprising to hear those comments.

A lot of our came from non cs backgrounds with a lot paper writing, pandas and numpy makes sense to them, its like writing Latex for them, polars is not. Its changing recently as we move towards more AI code but thats how/why things used to be.

I think their strategy right now is just spend as much raw cash as possible into companies

A bit surprised about the datafusion results from the post, I have tried it time and time again, but datafusion has always been the leading/trading blowers with polars for our workfloads with duckdb being vastly slower.

Yes and no, its not replacing the reason why pandas was popular ie data scientists, but it a full replacement of its pipeline usage, And I would saw also beating out spark

So you mean people with contracts are having their contracts honoured.... thats just normal business.

Kinda they do? Atleast our company knows the exact location of ours

Its not there because a lot of the providers werent providing until recently, and higher spatial resolutions, referesh times and higher compute costs meant that no one used them in prod. We tried with ECMWF-AIFS however ENS + IFS was bettter better.

One is a AI company first, the other has tons of other services and they can just buy those outright.

Everytime someone is using httpx I always wonder why use that especially when niquests exists? I feel like it has always been such a better package

Also niquests as well which is a dropin replacement and imo wayyy better than httpx

idk man, we pay stupid amounts of money to microsoft, we are an enterprise customer, expecting better availablity compared to my laptop isnt really a high bar.

My company is not even the same ballpark, but even we already have people for it. And doesn't include the fact that you can get a colo location and just a MSP or a contractor to do it for you

For not being stuck at any shenanigans that might happen at East Croydon.

Its gatwick, 3 lines go here, one every 15 minutes. I have always taken the train to either Heathrow or Gatwick, there's always been more than enough space to accomodate luggage, idk what people here are on about.

>But in general now trend is to use normal databases from IT world in OT world to overcome some legacy solutions.

Someone tell my company this please ;-;

Based on us keeping a replica of our AVEVA data archive in delta tables which are just parquets; and only compressing the parquets in LZ4 and getting a fuck ton of more space savings. I would say yes. Especially when you're onprem with a dedupe storage appliance.

I think swinging door made sense when storage was slow and scrace, but nowadays storage is cheap (even with these prices)