Where is the upside here? An alu plant probably provided more jobs and produced something of actual utility. This is burning power for no benefit to society.
It's burning less power than before, but it's not producing anything of value.
The world cannot reasonbly run without alu, it got along better without crypto currencies.
Oh, I agree. I lived nearby (working for ERCOT; the Texas Power Grid operator) when Alcoa was still there and was planning the shutdown. It seems about half the people in Rockdale worked for either Alcoa, the nearby coal power plant, or the nearby coal mine that fed the power plant.
I remember the local press going on about the crypto mining operation and how folks were going get high-tech jobs in this rural area of Texas. Of course it didn't go that way.
Aluminum smelting is an incredibly energy intensive operation. A lot of places in the US that used to host aluminum smelters now host large datacenters, include the Google data center in The Dalles, Oregon on the Columbia river near a hydro dam. It's a shame that Rockdale didn't get something useful like these other places.
As far as Al smelting in the US; I don't know. I'd imagine it produces a lot of air pollution by itself and uses huge amounts of power that is usually generated by cheap methods like burning rocks (coal) or large hydro operations nearby to minimize transmission costs. Then you gotta get ore to the site. The only Al smelter I recall being left in the US is up near Puget Sound in Bellingham, WA and I think it's currently shutdown.
> I remember the local press going on about the crypto mining operation and how folks were going get high-tech jobs in this rural area of Texas. Of course it didn't go that way.
That's a disappointingly common crypto industry lie. Cryptocurrency mining involves very little labor beyond initial construction; it's certainly not a major source of permanent employment.
I assume uploading to arXiv doesn't count as having published a peer reviewed journal article, which is a problem for professionals.
For example, for me to progress in my current job I either need a doctorate or to have published a number of peer-reviewed articles in recognised journals as first author. I have written two IETF RFCs and these count for nothing.
I am not a scientist, I am a software developer. I am not employed as a scientist, I am employed as a software developer. But the rules of the organisation are thus.
> I assume uploading to arXiv doesn't count as having published a peer reviewed journal article, which is a problem for professionals.
Yes, in fact this is mainly what I meant with "quality badge". It's a badge mostly for instutitional bean-counting processes. Fellow scientists don't need it that much, typically we can separate the wheat from the chaff with a very quick skim.
This is a massive pain at my work, same as I'm sure most other comments are saying.
I use Platform.IO for firmware development and can't build my firmware unless I hotspot my phone. I would say that's a PIO bug unless there is a flag I don't know about, but it's exposed by this nuisance of a firewall.
The devs where I am spend much of our time hotspotted to our phones with the corporate network never connected so IP goes out over the mobile network.
Whenever possible we use 'do not verify server certs' flags in libs and commands which is not ideal.
I have been programming for over 30 years on all sorts of systems and the Pandas DataFrame API is completely beyond me. Just trying to get a cell value seems way more difficult than it should be.
Same with xarray datasets.
I just loaded the same CSV into Pandas and Polars and Polars did a much better job of it.
To me, Polars feels like almost exactly how I would want to redesign Pandas interfaces for small - medium sized data processing, given my previous experience with Pandas and PySpark. Throw out all the custom multi index nonsense, throw out numpy and handle types properly, memory map arrow, focus on method chaining interface, do standard stuff like groupby and window functions in the standard way, and implement all the query optimizations under the hood that we know make stuff way faster.
To be fair, Polars has the benefit of hindsight and designing their interfaces and syntax from scratch. The poor choices in Pandas were made long ago, and its adoption and evolution into the most popular dataframe library for python feels like mostly about timing the market than having the best software product.
The whole thing though is just shockingly unhelpful and half-baked for something that taken over so completely in its niche. I guess my perspective is that of someone trying to build reliable automation, though—it’s probably really nice if you’re just noodlin’ in notebooks or the repl or whatever.
Multi indexes is baking my data into the horrible pandas object model into weird tuples. Every gripe I’ve had with pandas starts with trying to do something “simple” then following the pandas object model to achieve it and over complicating things. Polars is awesome, it fits my numpy understanding of dataframes as dict labeled arrays. I even like the immutability aspect.
Accessing an individual cell value is slightly clunky, but that's not really where you use a DataFrame. A DataFrame is an object for where the entirety of the dataset is under study. Where you are typically interested in the broad distributions contained within the data.
After you have highlighted trends (the majority of the work), then you might go spelunking at individual examples to see why something is funny.
It's burning less power than before, but it's not producing anything of value.
The world cannot reasonbly run without alu, it got along better without crypto currencies.
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