rbartelme

Yeah, I definitely do something similar with my personal projects.

I come from more of a hardware & environmental engineering background and we were always taught that projects were iteratively built via Design, Build, Test, Learn cycles.

I drive the Design and basic skeleton of the build (pseudocode or boilerplate), then pass off the rest of the Build and Test to the agent. I pick up after the test and read the agent commits/notes, then write up next steps. Repeat DBTL. Maybe spin a few features out at a time in parallel depending on how much time I want to devote to reviewing new project features later in the day.

Pretty cool bioinformatics algorithm to speed up what was traditionally a dynamic Burrows-Wheeler Transform. Interested to see where this gets implemented outside of benchmarking in the next few years!

Thanks for the perspective. This makes me think I was a bit quick to judge their methodologies.

It's somewhat interesting, but the authors' conclusions are a bit odd given their data.

They acknowledge that fame is potentially confounding: Risk factors (impulsivity, substance use, etc.) -> Fame achievement | Risk factors -> Early mortality

The authors also appear to conclude that fame is semi-causal of the mortality risk. If, taking a causal statistical approach, the authors conditioned on the collider:

Risk factors (substance use, personality traits, mental health vulnerabilities) -> Becoming/staying a professional singer <- Talent/drive toward fame

I do applaud them for preregistering the study, but I think this paper needed a little more rigor in peer review.

Outside bioconductor or the tidyverse in R can be just as unstable due to CRAN's package requirements.

This is a non-issue with Polars dataframes to_pandas() method. You get all the performance of Polars for cleaning large datasets, and to_pandas() gives you backwards compatibility with other libraries. However, plotnine is completely compatible with Polars dataframe objects.

> Life's two most fundamental properties are homeostasis and reproduction. > The loss of these two combined with its parasitic nature makes this cell a form on non-life.

This is a decidedly Eukaryote-centric take. Homeostasis in higher mammals is a complex network of genes -> RNA -> proteins -> metabolic pathways

Reproduction is also far more simple in organisms with binary fission cellular division.

A more appropriate scientific term would be obligate commensalism vs. "parasitic". That actually encapsulates their need for metabolic precursors from the host, but allows for tRNA, rRNA, origin of replication, etc...present in the organism's genome.

Yeah I think this is definitely the future. Recently, I too have spent considerable time on probabilistic hyper-graph models in certain domains of science. Maybe it _is_ the next big thing.

Sometimes I miss the benchwork, but moving into bioinformatics/data science full-time has been much better for my physical and mental health.