An ebook published using TiddlyWiki
For example, type
search textin the standard search and select one of the results, or just click outside of it (to hide the popup list): each of the words you search for will be highlighted separately in the text of the tiddlers.
TiddlyBlink is an adaptation of TiddlyWiki with the goal of helping you see connections between your ideas, and move quickly from one idea to another. It was inspired by the bi-directional linking found in Roam (https://roamresearch.com/), but built with capabilities already available in TiddlyWiki (https://tiddlywiki.com). See my example file here.
Feather is a collection of simply beautiful open source icons. Each icon is designed on a 24x24 grid with an emphasis on simplicity, consistency and readability.
Inspired by Pomodoro Technique time management tools, Marinara online timers are customizable to meet your productivity goals. By digital agency 352 Inc.
Have you ever had the feeling that your head is not quite big enough to hold everything you need to remember?
This is a website that I made about cocktails. I'm not a huge cocktail nerd (drinking is bad, probably), but think that they're cool. And the world's pretty bad right now and making this has been calming.
It gave me a chance to both tinker with technology I usually don't use (Elm), and explore some of the cool properties of cocktails: notably that they're pretty similar and have standardized ingredients, so they can be described in relationship to each other.
So some of it might seem funky. By default, the list is sorted by 'feasibility': as you add ingredients that you have, it'll put recipes that you can make (or barely make) closer to the top. Also, click on 'Grid' for a wacky adjacency grid of cocktails and their ingredients.
Also, for vim fans, there’s j & k support.
compulsively made a thing because my anxiety level is ‘pinned to the fucking roof’, here it is, it’s a cocktail recipe browser built in elm that can do things like show similar recipes and stuff https://t.co/RDjJ0V3aEH pic.twitter.com/GiRIx4huiK
— Tom MacWright (@tmcw) March 16, 2020
These are sites with resources created by the TiddlyWiki Community help you get the best out of TiddlyWiki: plugins, macros and more. Submit new entries via GitHub, Twitter or by posting in the TiddlyWiki Groups.
Please feel free to click around here and explore. Don't expect too much in the way coherence or permanence… it is a lot of half-baked ideas, badly organised. The very purpose is for snippets to percolate and morph and evolve over time, and it's possible (quite likely) that pages will move around.
That said, I make it public in the interest of info-sharing, and occassionally it is quite useful to have a public place to refer someone to an idea-in-progress of mine.
Some more info on the whats and the whys.
I’ll have to take a look at this sort of set up while I’m looking at wikis. I’m sort of partial to TiddlyWiki myself so far.
Plugin to display a disclaimer on posts in a draft category.
We are used to the availability of big data generated in nearly all fields of science as a consequence of technological progress. However, the analysis of such data possess vast challenges. One of these relates to the explainability of artificial intelligence (AI) or machine learning methods. Currently, many of such methods are non-transparent with respect to their working mechanism and for this reason are called black box models, most notably deep learning methods. However, it has been realized that this constitutes severe problems for a number of fields including the health sciences and criminal justice and arguments have been brought forward in favor of an explainable AI. In this paper, we do not assume the usual perspective presenting explainable AI as it should be, but rather we provide a discussion what explainable AI can be. The difference is that we do not present wishful thinking but reality grounded properties in relation to a scientific theory beyond physics.
Over recent years, new light has been shed on aspects of information processing in cells. The quantification of information, as described by Shannon’s information theory, is a basic and powerful tool that can be applied to various fields, such as communication, statistics, and computer science, as well as to information processing within cells. It has also been used to infer the network structure of molecular species. However, the difficulty of obtaining sufficient sample sizes and the computational burden associated with the high-dimensional data often encountered in biology can result in bottlenecks in the application of information theory to systems biology. This article provides an overview of the application of information theory to systems biology, discussing the associated bottlenecks and reviewing recent work.