Wox VS WaveFunctionCollapse

RunFlow is a cross-platform productivity tool which can launch apps and search files and more, that similar to Wox and PowerToys on Windows, and also similar like Alfred and Raycast on macOS. But we have differences with these tools, and we have our own unique new features. Right now, at the below, we will introduce you what features of RunFlow have been implemented in more details. It's an amazing journey, let's start.

I find Wox (which also uses Everything) a really great quick launcher, it saves a lot of time.

https://github.com/Wox-launcher/Wox

Maybe it's Wox (https://github.com/Wox-launcher/Wox) which is no longer maintained. Out of all these Spotlight alternatives, I think Fluent search is the best https://github.com/adirh3/Fluent-Search

Wox

I shared the exact same sentiment when I transitioned to Windows after a long period of only using macOS. I eventually began building a launcher myself using C# and over time it has become my daily driver. Feel free to check it out at https://github.com/dkgv/pinpoint, PRs welcome :-)

A few other nice candidates I found include:

- Keypirinha: https://keypirinha.com/

- Wox: https://github.com/Wox-launcher/Wox

- ueli: https://github.com/oliverschwendener/ueli

Other options that are open source are wox, cerebro and flow launcher

Have a look at PowerToys Run - based on Wox, open-source, and officially MS-backed.

For anyone in need of a functional search in windows: I recommend Wox, it uses the everyhig searoh engine and it works flawlessly

Personally I avoid the start menu or anything related to Windows indexing. What I use is everything and wox.

Thank you! And yes, I agree. I was looking at uh https://github.com/mxgmn/WaveFunctionCollapse and wondering if that were applicable here :)

Have a good day!

Intuitively, it measures the difference between two probability distributions. It's not symmetric, so it's not quite that, but in my opinion, it's good intuition.

As motivation, say you're an internet provider, providing internet service to a business. You naturally want to save money, so you perhaps want to compress packets before they go over the wire. Let's say the business you're providing service to also compresses their data, but they've made a mistake and do it inefficiently.

Let's say the business has, incorrectly, determined the probability distribution for their data to be $q(x)$. That is, they assign probability of seeing symbol $x$ to be $q(x)$. Let's say you've determined the "true" distribution to be $p(x)$. The entropy, or number of bits, they expect to transmit per packet/symbol will be $-\sum p(x) lg(q(x))$. Meaning, they'll compress their stream under the assumption that the distribution is $q(x)$ but the actually probability of seeing a packet, $x$, is $p(x)$, which is why the term $p(x) lg(q(x))$ shows up.

The number of bits you're transmitting is just $-\sum p(x) lg(p(x))$. Now we ask, how many bits, per packet, is the savings of your method over the businesses? This is $-\sum p(x) lg(q(x)/p(x))$, which is exactly the Kullback-Leibler divergence (maybe up to a sign difference).

In other words, given a "guess" at a distribution and the "true" distribution, how bad is it between them? This is the Kullback-Leibler distribution and why it shows up (I believe) in machine learning and fitness functions.

As a more concrete example, I just ran across a paper talking [0] about using WFC [1] to asses how well it, and other algorithms, do when trying to create generative "super mario brothers" like levels. Take a 2x2 or 3x3 grid, make a library of tiles, use that to generate a random level, then use the K-L divergence to determine how well your generative algorithm has done compared to the observed distribution from an example image.

[0] https://arxiv.org/pdf/1905.05077.pdf

[1] https://github.com/mxgmn/WaveFunctionCollapse

Reminds me of wave function collapse - a programmatic way to generate mazes.

I am using WFC to generate the terrain, with pretty much a copy-paste implementation of the original WFC implemented into Unity.

If you still want to go the WFC route, the original WFC repository is a great place to start. There's also a (relatively barebones looking) Godot plugin you could take a look at.

wave function collapse studies - this is done with the https://github.com/mxgmn/WaveFunctionCollapse algorithm after I saw https://github.com/CodingTrain/Wave-Function-Collapse mention it. done in P5! IG https://www.instagram.com/ronivonu/

There’s an interesting approach similar to this called [Wave Function Collapse](https://github.com/mxgmn/WaveFunctionCollapse) (no relation to wfc in physics idea besides inspiration). It can infer the probabilistic constraints from one input example, and it seems to generalize quite well. Here’s a [little demo](https://oskarstalberg.com/game/wave/wave.html)