This makes me wonder about adversarial interoperability: are there any attachments by other brands? Given they’ve been around for about 100 years, is there an ecosystem of other things that can work with the KitchenAid?
What’s remarkable is that KitchenAid supports “cross-generational attachment compatibility” meaning that attachments from the 1930s can be used on modern mixers. In an era when phone charger standards change with the season, this is a commendable buttress against obsolescence.
(This is the first response I’m making directly from my microsub client with embedded micropub client. Pleased that it is now working as POC.)
Yesterday our 5yo complained from the couch ‘I’m never going to get this right again, there’s just too many options’ while holding up her 3×3 Rubik’s Cube up to me. In the evening I opened up my feedreader and found this guide. As a kid I never learned how to solve one of these. Usually I took a screwdriver to the thing in the end, prying loose a corner and then the entire thing, rebuilding it ‘solved’. But now decades on, maybe I can finally learn to solve it with our 5yo. Reading Kev’s blogpost it will be a month or two before we can see results.
That isn’t the aim of this guide. Instead, what I want to do is simply demonstrate how I break the process of solving the Rubik’s Cube down so that you can do it too.
Interesting line of thought about when contriarianism makes sense. I wonder about a connection to counter factual thought experiments as a sorting mechanism for such positions, and about probes and experiments to explore contrarian positions in practice, where the cost of experimentation is low, but the results help see if the contrarian position has merit.
Consider opinions distributed over a continuous parameter, like the chance of rain tomorrow. Averaging over many topics, accuracy is highest at the median, and falls away for other percentile ranks. This is bad news for contrarians, who sit at extreme percentile ranks. If you want to think you are right as a contrarian, you have to think your case is an exception to this overall pattern, due to some unusual feature of you or your situation.
Yet I am often tempted to hold contrarian opinions. In this post I want to describe the best case for being a contrarian.
Research Rabbit is a tool that, when provided with some academic paper you already are familiar with, can suggest other related material as well as provide that material. By looking for material from the same authors, by following the references, and by looking at the topics. This can speed up the discovery phase quite a lot I think. (And potentially also further increases the amount of stuff you haven’t looked at but which sounds relevant, thus feeding the collector’s fallacy.).
I’ve created an account. It can connect to Zotero where you already have your library of papers you are interested in (if you use Zotero with an account. I use Zotero standalone at the moment I added a Zotero account and storage subscription to sync with Research Rabbit).
Multiple elegant ideas (and practices) in that post, about the use of Excalidraw within Obsidian (which I previously described):
1) creating icons from basic forms (such as sketch noting teaches as well) and iterate each time you use them
2) keep your icons in a library in Excalidraw for various forms of re-use and for iteration
3) add #keywords to your icon, because in Excalidraw/Obsidian these behave as active searches for those keywords just like regular # in a text.
Because I couldn’t even get past the level of drawing stick figures, I have always felt intimidated by friends who could draw well. The idea of developing my visual vocabulary was a game-changer for me…… I added hashtags to each icon because, this way, if you add them to your sketch in the Obsidian-Excalidraw plugin, your drawing will be tagged with the relevant keywords.
I am looking forward to reading this. Will need to put aside some time to be able to really focus, given the author, and the amount of time taken to write it.
…an article I worked on for a couple of years. It’s only 2,200 words, but they were hard words to find because the ideas were, and are, hard for me. … The article argues, roughly, that the sorts of generalizations that machine learning models embody are very different from the sort of generalizations the West has taken as the truths that matter.