Pozorvlak on Nostr: nprofile1q…d6kk0 my perspective on this comes from having worked in (a) an ...

nprofile1qy2hwumn8ghj7un9d3shjtnddaehgu3wwp6kyqpqjd874v63430gng67kpw5m597f34dr9vr0yhkp8dkmnma8208raysyd6kk0 (nprofile…6kk0) my perspective on this comes from having worked in (a) an empirical field (computer architecture), and (b) industry. In the former case, a paper looks like "abstract; literature review; methods; graphs; conclusions". I want the methods to be as clearly stated as possible and the graphs to be readable. Papers in most empirical fields are like this; comparch is perhaps unusually simple in that the motivation is always "we tried this because we thought it might make Number Go Up", and the conclusion is always "Number Went Up by X% under circumstances that probably don't apply in your case, sorry." In the latter case, I've been known to tear through a dozen papers in a day from authors whose names I forget as soon as I type them into a code comment reading "# algorithm taken from Snooks and FitzHaber 1973, runs in O(keys * bases^2) time", and I skip over as much of the text as possible looking for the pseudocode, dammit, I've got a problem to solve.