Locate the section for your github remote in the .git/config file. It looks like this:
[remote "origin"]
fetch = +refs/heads/*:refs/remotes/origin/*
url = git@github.com:joyent/node.git
Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/* to this section. Obviously, change the github url to match your project's URL. It ends up looking like this:
| #!/bin/sh | |
| echo "What should the Application be called (no spaces allowed e.g. GCal)?" | |
| read inputline | |
| name="$inputline" | |
| echo "What is the url (e.g. https://www.google.com/calendar/render)?" | |
| read inputline | |
| url="$inputline" |
Audience: I assume you heard of chatGPT, maybe played with it a little, and was imressed by it (or tried very hard not to be). And that you also heard that it is "a large language model". And maybe that it "solved natural language understanding". Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.
Around 2014-2017, right within the rise of neural-network based methods for NLP, I was giving a semi-academic-semi-popsci lecture, revolving around the story that achieving perfect language modeling is equivalent to being as intelligent as a human. Somewhere around the same time I was also asked in an academic panel "what would you do if you were given infinite compute and no need to worry about labour costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We
| license: gpl-3.0 |
Get Homebrew installed on your mac if you don't already have it
Install highlight. "brew install highlight". (This brings down Lua and Boost as well)
| *.acn | |
| *.acr | |
| *.alg | |
| *.aux | |
| *.bak | |
| *.bbl | |
| *.bcf | |
| *.blg | |
| *.brf | |
| *.bst |
The range sliders at the top change the values for the force-directed algorithm and the buttons load new graphs and apply various techniques. This will hopefully serve as a tool for teaching network analysis and visualization principles during my Gephi courses and general Networks in the Humanities presentations.
Notice this includes a pretty straightforward way to load CSV node and edge lists as exported from Gephi.
It also includes a pathfinding algorithm built for the standard data structure of force-directed networks in D3. This requires the addition of .id attributes for the nodes, however.
Now with Clustering Coefficients!
Also, it loads images for nodes but the images are not in the gist. The code also refers to different network types but the data files on Gist only refer to the transportation network.
