Typescript allow initializer to create and instantiate an instance



The keyword partial transform fields into optional.




So we're able to initialize our object instance this way.




Pretty cool eh..



Comments

Post a Comment

Popular posts from this blog

gemini cli getting file not defined error

Image
 While trying to run gemini cli on my windows linux subsystem, i bump into this error here - "/Roaming/npm/node_modules/@google/gemini-cli/node_modules/undici/lib/web/webidl/index.js:512 webidl.is.File = webidl.util.MakeTypeAssertion(File)"  "ReferenceError: File is not defined" In Node.js, there’s no built-in File (until very recent Node 20+ with experimental WHATWG APIs). So, unless polyfilled, File is undefined, leading to the ReferenceError. And to resolve this, I had to upgrade to node 22 (might work for node 20) and then I was able to run it.
Read more

mongosh install properly

To install mongosh property on ubuntu 24.04, we need to wget -qO- https://www.mongodb.org/static/pgp/server-8.0.asc | sudo tee /etc/apt/trusted.gpg.d/server-8.0.asc Then run  echo "deb [ arch=amd64,arm64 ] https://repo.mongodb.org/apt/ubuntu noble/mongodb-org/8.0 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-8.0.list Next we will run  sudo apt-get update And then finally install it using  sudo apt-get install -y mongodb-mongosh To verify if it is running we run  mongosh --version
Read more

llama cpp running it in google colab

Image
There are 2 options to do this in Google  colab.  Option 1 (Easiest)  With this option, we can just install the relevant package and then run the model.  First we need to install the required packages using the following command:-  ! pip install -U llama-cpp-python Next,  we will try to get the model using the following command # !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained(   repo_id= "unsloth/Qwen3-0.6B-GGUF" ,   filename= "Qwen3-0.6B-IQ4_NL.gguf" , ) We can see the model being downloaded:- And then we will run the following command to test this model llm.create_chat_completion(   messages = [     {       "role" : "user" ,       "content" : "What is the capital of France?"     }   ] ) And the output will look something like this:-  Option 2 llama.cpp is a powerful inference engine and if you wanted to get it running in google colab, you co...
Read more