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  • 16 comments
Joined 3 years ago
Cake day: December 1st, 2023
  • Huh? Both hover to focus and click button in background work in macOS, though hover to focus usually requires an external application. There used to be a focus follows mouse that you could enable via a terminal command, but Apple removed it.

    The top menu bar kind of seems to be more of a result of historical happenstance, and maybe some different philosophies regarding Fitts law.

    Bill Atkinson, who designed the UX for the Apple Lisa recounts that part of the decision was to avoid the problem of menu items being possibly obscured. If the window of some application is near the bottom or partially off the desktop, the menu bar of individual windows can become obscured and inaccessible.

    Historically the menu bar would’ve been easier for normal people to learn due to consistency, and also helped with limited screen estate.

    Memories of Lisa - CHM - https://computerhistory.org/blog/memories-of-lisa/

  • IIRC there were some polls for how helpful LLMs were by language/professions, and data science languages/workflows consistently rated LLMs very highly. Which makes sense, because the main steps of 1) data cleaning, 2) estimation and 3) presenting results all have lots of boilerplate.

    Data cleaning really just revolves around a few core functions such as filter, select, and join; joins in particular can get very complicated to keep track of for big data.

    For estimation, the more complicated models all require lots of hyperparameters, all of which need to be set up (instantiated if you use an OOP implementation like Python) and looped over some validation set. Even with dedicated high level libraries like scikit, there is still a lot of boilerplate.

    Presentation usually consists of visualisation and cleaning up results for tables. Professional visualisations require titles, axis labels, reformatted axis labels etc, which is 4-5 lines of boilerplate minimum. Tables are usually catted out to HTML or LaTeX, both of which are notorious for boilerplate. This isn’t even getting into fancier frontends/dashboards, which is its own can of worms.

    The fact that these steps tend to be quite bespoke for every dataset also means that they couldn’t be easily automated by existing autocomplete, e.g. formatting SYS_BP to “Systolic Blood Pressure (mmHg)” for the graphs/tables.

  • Unfortunately I find even prompts like this insufficient for accuracy, because even when directly you directly ask them for information directly supported by sources, they are still prone to hallucination. The use of super blunt language as a result of the prompt may even further lull you into a false sense of security.

    Instead, I always ask the LLM to provide a confidence score appended to all responses. Something like

    For all responses, append a confidence score in percentages to denote the accuracy of the information, e.g. (CS: 80%). It is OK to be uncertain, but only if this is due to lack of and/or conflicting sources. It is UNACCEPTABLE to provide responses that are incorrect, or do not convey the uncertainty of the response.

    Even then, due to how LLM training works, the LLM is still prone to just hallucinating the CS score. Still, it is a bit better than nothing.