When you want to run stuff client side instead of your server is one question to determine.
For R specifically, it is focused on stats/graphing. So if you wanted an app where someone could upload data and fit a complicated regression model, this would be a good use case. (There are probably javascript libraries for regression, but if willing to live with the bit of start up lag, worth it for anything mildly complicated -- factors in R for example would not like to worry about writing my own code in javascript to make the design matrix.)
In the case where you run the server, the data has to travel to your server, your computer estimates the model, and it sends it back. WASM apps this all happens client side.
It is a good use case for dynamic graphs/dashboards as well. If the data is small enough to entirely fit in memory, can basically have a local interactive session with the data and everything will be quite snappy (do not need to continually go back and forth with your server to query data).
One nice thing about Quarto, is that I could have different fonts and formatting niceties for the epub vs PDF version (which the PDF version is the one I use to sell a paperback copy).
Additionally wrote a little script to auto-translate the contents from the markdown, so currently have the book available in Spanish and French as well.
I don't know what NIST says, but for the tests that use Chi-square, you can look at the left tail. Basically tests that have very small Chi-square values are "too close" to the expected distribution.
This is how Fisher critiqued Mendel's experiments -- they were too perfect!
For the more specific part that the PD states it does not physically save the data locally, I do work with cities Police departments and Flock will integrate with the local record management system.
I suspect they probably do have the data locally integrated (at least for the time period the state allows them to retain the records). But even if they do not, many police departments that would not be an excuse (although you need to request fast, many states only retain for 30 days or less now).
This is very obnoxious when transferring documents from Gemini to google docs (and not just math, tables/code sections are often not transferred correctly as well).
I do remember 1 example of an emoji in tech docs before all of this -- learning github actions (which based on my blog happened in 2021 for me, before ChatGPT release), at one point they had an apple emoji at the final stage saying "done". (I am sure there are others, I just do not remember them.)
But agree excessive emoji's, tables of things, and just being overly verbose are tells for me anymore.
I do recall emoji use getting more popular in docs and – brrh – in the outputs of CLI programs already before LLMs. I’m pretty sure thst the trend originated from the JS ecosystem.
It absolutely was a trend right before LLM training started — but no way this was already the style of the majority of all tech docs and PRs ever.
The „average“ style, from the Unix manpages from the 1960s through the Linux Documentation Project all the way to the latest super-hip JavaScript isEven emoji vomit README must still have been relatively tame I assume.
Can folks comment on what applications they use k-means for? It was a basic technique I learned in school, but honestly I am not really familiar with a single use case that is very clearly motivated besides "pretty pictures".
So I do a bit of work in geospatial analysis, and hotspots are better represented by DBSCAN (do not need to assign every point a cluster). I just do not even use clustering very often in gig (supervised ML and anomaly detection are much more prevalent in the rest of my work).
Oh nice, thank you for that tip. I was doing the opposite, `new_obj = mod.Class(...)` and then assigning the dicts from the old object (which was when I realized the pickle save/load was easier).
For R specifically, it is focused on stats/graphing. So if you wanted an app where someone could upload data and fit a complicated regression model, this would be a good use case. (There are probably javascript libraries for regression, but if willing to live with the bit of start up lag, worth it for anything mildly complicated -- factors in R for example would not like to worry about writing my own code in javascript to make the design matrix.)
In the case where you run the server, the data has to travel to your server, your computer estimates the model, and it sends it back. WASM apps this all happens client side.
It is a good use case for dynamic graphs/dashboards as well. If the data is small enough to entirely fit in memory, can basically have a local interactive session with the data and everything will be quite snappy (do not need to continually go back and forth with your server to query data).
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