virattt

astuyve

{
"Version": "2012-10-17",
"Statement": [
    {
        "Effect": "Deny",
        "Action": [
            "logs:CreateLogGroup",
            "logs:CreateLogStream",
            "logs:PutLogEvents"
        ],

yuinchien

TERM	DEFINITION	SOURCE	LINK
AGI. Artificial General Intelligence	An AGI could learn to accomplish any intellectual task that human beings or animals can perform. Alternatively, AGI has been defined as an autonomous system that surpasses human capabilities in the majority of economically valuable tasks.	Source
Adversarial suffix	A string of random seeming characters, to a prompt that makes the LLM significantly more likely to return an unfiltered response.	Source	Demo
AI. Artificial Intelligence	Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is a field of study in computer science which develops and studies intelligent machines.	[Source](https://en.wikipedia.org/wiki/Artificial

ZeldOcarina

import * as path from "path";
import * as fs from "fs";
import axios, { AxiosError, type AxiosInstance } from "axios";

import airtableDefaults from "./constants/airtable-defaults";

const fsPromises = fs.promises;

const MAX_RETRIES = 3; // Number of times to retry after hitting rate limit
const RETRY_DELAY = 30 * 1000; // 30 seconds in milliseconds

hwchase17

from langchain.document_loaders import YoutubeLoader
from langchain.indexes import VectorstoreIndexCreator
urls = [
    ("https://www.youtube.com/watch?v=fP6vRNkNEt0", "Prompt Injection"),
    ("https://www.youtube.com/watch?v=qWv2vyOX0tk", "Low Code-No Code"),
    ("https://www.youtube.com/watch?v=k8GNCCs16F4", "Agents In Production"),
    ("https://www.youtube.com/watch?v=1gRlCjy18m4", "Agents"),
    ("https://www.youtube.com/watch?v=fLn-WqliEQU", "Output Parsing"),
    ("https://www.youtube.com/watch?v=ywT-5yKDtDg", "Document QA"),
    ("https://www.youtube.com/watch?v=GrCFyyyAxCU", "SQL"),

mattdesl

Using data and code from: https://graphics.geometrian.com/research/spectral-primaries.html

See their reference implementation for details (MIT license): https://github.com/geometrian/simple-spectral

This converts sRGB triplet into spectral reflectance, then combines two curves with weighted geometric mean to produce a "mixed" colour.

Installation

alex-streza

import { Provider } from "next-auth/providers";

export const FigmaProvider: Provider = {
  id: "figma",
  name: "Figma",
  type: "oauth",
  authorization: {
    url: "https://www.figma.com/oauth",
    params: {
      scope: "file_read",

JohnPhamous

export const chaosTestStrings = (): void => {
  const textNodes = getAllTextNodes(document.body);

  for (const node of textNodes) {
    const textNodeLength = node.textContent ? node.textContent.length : 0;
    if (node.textContent === null) {
      return;
    }
    if (node.parentElement instanceof Element) {
      if (node.parentElement.dataset.originalText === undefined) {

soupaulogomes

[
  {
    "gameTime": "2022-11-20T16:00:00Z",
    "homeTeam": "cat",
    "awayTeam": "equ"
  },
  {
    "gameTime": "2022-11-21T13:00:00Z",
    "homeTeam": "ing",
    "awayTeam": "ira"

madebyollin

# ------------------------------------------------------------------
# EDIT: I eventually found a faster way to run SD on macOS, via MPSGraph (~0.8s / step on M1 Pro):
#   https://github.com/madebyollin/maple-diffusion
# The original CoreML-related code & discussion is preserved below :)
# ------------------------------------------------------------------

# you too can run stable diffusion on the apple silicon GPU (no ANE sadly)
#
# quick test portraits (each took 50 steps x 2s / step ~= 100s on my M1 Pro):
# * https://i.imgur.com/5ywISvm.png