386 karma · joined July 12, 2020
The houses here used to be affordable for a middle income person or family. Now they aren't, that's the issue, not the sticker price.
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At the same time houses have gone up in price, wages have stagnated, rent has increased and inflation has increased. So we earn the same, pay more for rent, utilities and food, and have to save more (10% deposit is usually the minimum).
The house 2 doors down was last sold for £80k about 20 years ago. The house over the road that has just gone up for sale is asking for £300k. I'm lucky that my salary is over the median, but a 3-bed semi detached is like 10x the median salary (£26k). The same sort of property that my parents generation bought for 5x the median salary back in the 90s.
There is also the NX project (numerical elixir) which compiles tensors to GPU code making it much faster for those particular usecases. https://github.com/elixir-nx This has things like explorer which is elixir bindings to polars, livebook which is like jupyter notebooks (with enforced execution order thang god), aswell as some machine learning libraries in axon and scholar.
There has also been some work with libraries like flow and broadway which allow pipelines to be constructed with the usual syntaxt `step1 |> step2 |> step3` but to be executed across cores (or cluster).
I can imagine orgs using a combination of the above tools to do some interested distrubuted data pipelining and ML.
It does have good FFI, like rustler for the elixir is great. It makes sure rust panics unroll into elixir exceptions and that takes some of the scariness of writing them.
That plus dirty schedulers means you don't have to be a good citizen and can write cpu intensive code in a faster language.
review 1: This is important for the field, fix x, y and z and it should be ready to publish
review 2: This adds absolutely nothing, they are missing references from research group a, research group a, research group a.
review 3: Looks good, fix a, b, and c, and add references to research group b, research group b, research group b.
review 4: looks good, but figure x doesn't seem to show what you say it does, fix it or the caption.
From there it was actually fairly simple to see who was reviewing based on which papers they said we were missing. With research group A in particular, they were working on similar stuff so we think we may have scooped them. But that's also a bit of the problem with reviews, subject matter experts are probably also doing similar research. And if they have a big ego it can potentially hamper work by other groups getting published.
Then your tester says it isn't correct in 2/3 places, you find out you've only updated the function in 1 place and either abstract those the other 2 cases out or update it in those 2 places aswell.
The full day cost around £6200, that included music, demonstration couple, compare, 6 judges, food, first aiders, venue hire, officials, trophies, medals, programmes, decorations. We then charged around £12.50 entry for ~500 students.
The national competition for universities happens in blackpool every year and I imagine the costs are around 3-4x that considering the venue and number of officials. And unless we got discounts for being universities, or the judges are paid less, i imagine it costs at least £28,000 to put on a big professional competition.
In a comparable venue to the winter gardens in blackpool
I didn't know of anybody who had done any Lisp or Ocaml in my time in academia (in chemistry, chemical engineering and biology departments), but that's just 3 universities, and i certainly didn't know everybody.
During my PhD I was running some simulations using poorly written python code. initially it would take several hours. In that time i could go to the lab, run some wetlab experiments and the results of my simulations would be there when i got back to the office. It was only taking python "home" and building some of my own projects that i learned how to 1. write more pythonic code and 2. write more performant code. Now i work for a software company.
If i'd have stayed in in academia I would probably still be writing quick and dirty code and not worrying about the runtime because as a researcher there is always something else you can be doing.
I got to dig in to some hotspots in our code, some hotspots in the test suite code and speed things up significantly. It's incredibly rewarding work. But as the blogpost says, you need to know how things work (and get good measurements) to be able to make a difference. Knowing what tradeoffs to make, such as caching things to trade off memory usage for computation vs reworking algorithms to be more effecient in the first place, is tricky and not something you can necessarily hand over to a junior dev.
Add in # %% and it makes the code executable in isolation. Like a cell in jupyter. This means i can write a py file. Execute bits indepentantly and when im ready to check it in just remove the block comments
The fact that its a dietary choice typically made by those who are more affluent and they are willing to pay for it.
Looking at some child comments it looks like there have been attempts to get it added to scipy since 2016.
[1]https://docs.scipy.org/doc/scipy/reference/generated/scipy.s...
[2]https://docs.scipy.org/doc/scipy/reference/generated/scipy.s...
Previous documents ive written with word i would do things like tight layouts on images, maybe with anchors, but that's a recipe for things moving around.
When i came to compile my chapters to a final document i used master document-subdocument to pull everything together. I only had a few issues with blank pages being added when exporting to pdf and that was due to my use of page breaks and section breaks.
If so it would be actually quite useful in science where old computers are used because of old software used to run old instruments. The old GC-FID i used during my PhD ran on windows 95. If that software could run on electron they could actually get some modern hardware to run those old machines
I was attending a show and tell following seeing this where an engineer was trying to isolate failure of certain functions from the rest of the system and i sent this to them.
No idea if they watched it but i became an OTP evangelist in that moment
Generally outside of work hours I'll throw together a vague example and validate that the idea is something that works and get vaguely familiar with the libraries involved. That way when I go to build it at work I don't start from zero. If its for a personal project i just noodle in a repl or ide on a branch
The exothermic reaction will also slow down when cooled which will further help contain the problem
However it's quite difficult to break its bonds in ways that yield useful compounds. And until there is a viable way of upcycling it, burning it for energy is what they do