Michael WANG Fei oceanumeric

Multiple SSH keys for GitHub and GitLab

1. Generate SSH keys

ssh-keygen -t ed25519 -C "[email protected]" -f ~/.ssh/id_rsa_github
ssh-keygen -t ed25519 -C "[email protected]" -f ~/.ssh/id_rsa_gitlab

2. Copy keys to GitHub and GitLab

Cloud GPUs: Servers, Providers & Everything You Would Ever Need

Your company's GPU computing strategy is essential whether you engage in 3D visualization, machine learning, AI, or any other form of intensive computing.

There was a time when businesses had to wait for long periods of time while deep learning models were being trained and processed. Because it was time-consuming, costly, and created space and organization problems, it reduced their output.

This problem has been resolved in the most recent GPU designs. Because of their high parallel processing efficiency, they are well-suited for handling large calculations and speeding up the training of your AI models.

When it comes to deep learning, GPUs can speed up the training of neural networks by a factor of 250 compared to CPUs, and the latest generation of cloud GPUs is reshaping data science and other emerging technologies by delivering even greater performance at a lower cost and with the added benefits of easy scalability and rapid deployment.

A non-exhaustive list of WebGL and WebGPU frameworks and libraries. It is mostly for learning purposes as some of the libraries listed are wip/outdated/not maintained anymore.

Engines and libraries

Name	Stars	Last Commit	Description
three.js	![GitHub Rep

	def construct_firm_profile(self, query: str, search_results: List[Dict], schema: str = "llm_store"):
	"""
	Construct firm profile based on search results
	"""

	datapoint = {
	"user_id": self.user_id,
	"search_query": query,
	"search_results": search_results,
	"created_at": datetime.now().isoformat(),

	var Twitter = require('node-tweet-stream')
	, t = new Twitter({
	consumer_key: '',
	consumer_secret: '',
	token: '',
	token_secret: ''
	});
	var watch = [
	"SB52",
	"SBLII",

	library(data.table)

	?`[.data.table`

	DT <- data.table(x=rep(c("b","a","c"),each=3), y=c(1,3,6), v=1:9)
	X <- data.table(x=c("c","b"), v=8:7, foo=c(4,2))

	colnames(DT)
	# [1] "x" "y" "v"