LifeIsStrange

Here's a list of mildly interesting things about the C language that I learned mostly by consuming Clang's ASTs. Although surprises are getting sparser, I might continue to update this document over time.

There are many more mildly interesting features of C++, but the language is literally known for being weird, whereas C is usually considered smaller and simpler, so this is (almost) only about C.

1. Combined type and variable/field declaration, inside a struct scope [https://godbolt.org/g/Rh94Go]

struct foo {
   struct bar {
 int x;

I've been deceiving you all. I had you believe that Svelte was a UI framework — unlike React and Vue etc, because it shifts work out of the client and into the compiler, but a framework nonetheless.

But that's not exactly accurate. In my defense, I didn't realise it myself until very recently. But with Svelte 3 around the corner, it's time to come clean about what Svelte really is.

Svelte is a language.

Specifically, Svelte is an attempt to answer a question that many people have asked, and a few have answered: what would it look like if we had a language for describing reactive user interfaces?

A few projects that have answered this question:

Using Git Commit Message Templates to Write Better Commit Messages

The always enthusiastic and knowledgeable mr. @jasaltvik shared with our team an article on writing (good) Git commit messages: How to Write a Git Commit Message. This excellent article explains why good Git commit messages are important, and explains what constitutes a good commit message. I wholeheartedly agree with what @cbeams writes in his article. (Have you read it yet? If not, go read it now. I'll wait.) It's sensible stuff. So I decided to start following the

Feature Matrix

UI for adding/removing files

Feature	GitHub	Phabricator	Notes
## Code

Create a file Image.js from the source below (it is almost a copy of Image.js from tiptap-extensions except that it has a constructor that accepts uploadFunc (function to be called with image being uploaded) and additional logic if(upload) { ... } else { ... previous base64 logic .. } in the new Plugin section.

import {Node, Plugin} from 'tiptap'
import {nodeInputRule} from 'tiptap-commands'

/**
 * Matches following attributes in Markdown-typed image: [, alt, src, title]
 *

	import spacy
	from spacy.matcher import Matcher

	Active = "Harry ate six shrimp at dinner.\
	Beautiful giraffes roam the savannah.\
	Sue changed the flat tire.\
	We are going to watch a movie tonight.\
	I ran the obstacle course in record time.\
	The crew paved the entire stretch of highway.\
	Mom read the novel in one day.\


	import com.fasterxml.jackson.databind.DeserializationFeature
	import com.fasterxml.jackson.databind.ObjectMapper
	import com.fasterxml.jackson.module.kotlin.readValue
	import org.junit.jupiter.api.Assertions.*
	import org.junit.jupiter.api.BeforeAll
	import org.junit.jupiter.api.Disabled
	import org.junit.jupiter.api.Test
	import org.junit.jupiter.api.TestInstance
	import org.junit.jupiter.api.extension.ExtendWith

	import kotlinx.coroutines.Dispatchers
	import kotlinx.coroutines.test.TestCoroutineDispatcher
	import kotlinx.coroutines.test.TestCoroutineScope
	import kotlinx.coroutines.test.resetMain
	import kotlinx.coroutines.test.setMain
	import org.junit.jupiter.api.extension.AfterAllCallback
	import org.junit.jupiter.api.extension.AfterEachCallback
	import org.junit.jupiter.api.extension.BeforeAllCallback
	import org.junit.jupiter.api.extension.ExtendWith
	import org.junit.jupiter.api.extension.ExtensionContext

	/**
	* A component that takes care of the bulking of elasticsearch indexing requests to accommodate near-real-time indexing
	*
	* It executes indexing requests when either a time limit or a size limit is reached.
	* It avoids blocking the current thread that inserts the records to be indexed.
	*/
	@Component
	public class ESBulkProcessor {

	private static final Logger logger = LoggerFactory.getLogger(ESBulkProcessor.class);

	from transformers import XLNetTokenizer, XLNetForQuestionAnsweringSimple
	import torch
	tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
	model = XLNetForQuestionAnsweringSimple.from_pretrained('xlnet-base-cased')
	input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
	print(f'Encoded sequence ids -- {input_ids.tolist()[0]}')
	# Encoded sequence ids -- [17, 11368, 19, 94, 2288, 27, 10920, 4, 3]

	start_positions = torch.tensor([1])
	end_positions = torch.tensor([3])