const shade = 100;
type Shade = 100;
<dialog open> | |
<form method="dialog"> | |
<p>Do you want to confirm your action?</p> | |
<div class="right"> | |
<input class="btn" type="submit" value="Ok" /> | |
<input class ="btn" type="submit" value="Cancel" /> | |
</div> | |
</form> | |
</dialog> |
var c = require('commitment'); | |
/* | |
Ok, before you start this, be committed to following | |
each step NON-STOP until you are done with all the instructions! | |
I'm assuming you are a mac os x user. | |
If you are using windows, sorry about that... | |
Install |
<div> | |
<h1>This is an <strong>example</strong> block of <strong>text</strong>.</h1> | |
<p>An SVG is used to <strong>emphasize</strong> a single word of a block of text by giving it an underline that uses an SVG. Semantically it is emphasized with a <code>strong</code> tag and visually it is emphasized with the SVG.</p> | |
</div> |
var icqLibrary = icqLibrary || {}; | |
icqLibrary.queue = icqLibrary.queue || []; | |
icqLibrary.queue.push(() => { | |
console.log('Queue 1'); | |
}); | |
icqLibrary.queue.push(() => { | |
console.log('Queue 2'); | |
}); |
// C++ bit . save it in an example.cpp file | |
#include "emscripten.h" | |
extern "C" { | |
inline const char* cstr(const std::string& message) { | |
char * cstr = new char [message.length()+1]; | |
std::strcpy (cstr, message.c_str()); | |
return cstr; | |
} | |
EMSCRIPTEN_KEEPALIVE | |
const char* getAMessage() { |
/* Using a JavaScript proxy for a super low code REST client */ | |
// via https://dev.to/dipsaus9/javascript-lets-create-aproxy-19hg | |
// also see https://towardsdatascience.com/why-to-use-javascript-proxy-5cdc69d943e3 | |
// also see https://github.com/fastify/manifetch | |
// also see https://github.com/flash-oss/allserver | |
// and https://gist.github.com/v1vendi/75d5e5dad7a2d1ef3fcb48234e4528cb | |
const createApi = (url) => { | |
return new Proxy({}, { | |
get(target, key) { |
@supports (-webkit-backdrop-filter: none) or (backdrop-filter: none) { | |
.blurred-container { | |
-webkit-backdrop-filter: blur(10px); | |
backdrop-filter: blur(10px); | |
} | |
} | |
/* slightly transparent fallback for Firefox (not supporting backdrop-filter) */ | |
@supports not ((-webkit-backdrop-filter: none) or (backdrop-filter: none)) { | |
.blurred-container { |
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
/* Ultra lightweight Github REST Client */ | |
// original inspiration via https://gist.github.com/v1vendi/75d5e5dad7a2d1ef3fcb48234e4528cb | |
const token = 'github-token-here' | |
const githubClient = generateAPI('https://api.github.com', { | |
headers: { | |
'User-Agent': 'xyz', | |
'Authorization': `bearer ${token}` | |
} | |
}) |