igg

This list seem to be based on production based accounting if emissions. Consumption-based per-capita emissions are available here for those curious: https://ourworldindata.org/grapher/consumption-co2-per-capit...

The 1 GB DRAM per 1 TB Flash is to store the Flash Translation Layer mapping from logical addresses of the host system to the physical address in Flash. The write cache is separate and much more limited in size.

It is Julia all the way down.

  julia> @which randn(1000)
  randn(dims::Integer...) in Random at ~/Software/Julia/share/julia/stdlib/v1.5/Random/src/normal.jl:210

  julia> @edit randn(1000)
  # Opens the normal.jl file in your editor

  julia> @run randn(1000)
  # Starts the debugger in VSCode

Digging deeper, at least one of the PRs [1] is based on a 2018 paper [2]

[1] https://github.com/JuliaLang/julia/pull/29240 [2] https://arxiv.org/abs/1805.10941

You could use a cronjob to add and commit all files at midnight.

A Mathematical Theory of Communication

The article was renamed "The Mathematical Theory of Communication" in the 1949 book of the same name, a small but significant title change after realizing the generality of this work.

While you still need some experimentation to get a plot right, following the grammar of graphics approach can make it quick and methodical. For any plot, I just need to answer a few questions:

* What kind of plot do I want? Bar chart? Scatter plot? Boxplot? Pick the geom based on this.

* What column goes on each axis? Do I need to transform the axis? For e.g., is log scale better?

* Do I split data by some other column? Use different colors/fill, shapes, or facets.

* Do I need any annotations to point out interesting parts of the plot?

GGPlot and Plotnine are joy to work with once you grok the ideas of tidy data and grammar of graphics. Since the API is consistent, experimentation can be quick:

* That scatter plot looks to busy? Change the geom to a boxplot.

* Want to see if an attribute has any predictive power? Assign it to any unused aesthetic.

Seaborn can provide some higher-level functions compared to plotnine/ggplot like pair-plot. But as you said, you need to lookup seaborn's documentation all the time to work with it.

Yup, that's correct.

The decompression step on the receiver does not need the knowledge of the pre-trained dictionaries; they are used only to bootstrap the compression step.

Highly recommend plotnine if you are looking for the python equivalent of ggplot and only need static visualizations.

tmux-resurrect seems to have an option to save bash history. When combined with tmux-continuum, it should happen seamlessly.

fzf [0] works pretty well for this.

[0] https://github.com/junegunn/fzf