chris-kruining

import { z, ZodDate, ZodNumber, ZodBoolean, ZodString, ZodObject, ZodDiscriminatedUnion, ZodEnum, ZodArray, ZodTypeAny, ZodLiteral } from "zod";

const textEncoder = new TextEncoder();
const textDecoder = new TextDecoder();

export async function serialize<T extends ZodTypeAny>(schema: T, data: z.infer<T>): Promise<ArrayBuffer> {
    let accumulation = new Uint8Array();

    for await (const k of _serialize(schema, data, '')) {
        accumulation = concatTypedArrays(accumulation, k);

bigmistqke

import { createSignal } from "solid-js";

const signal = (buffer: Int32Array, index: number) => {
  const s = createSignal(undefined, { equals: false });

  const read = (debug?: string) => {
    s[0]();
    return buffer[index];
  };
  const write = (value: any) => {

rain-1

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

Steven24K

/**
 * Force directed graph layout algorithm according to Fruchterman and Reingold's principle. 
 * The algorithm can be summarized as follows: 
 * algorithm SPRING(G:graph);
 * place vertices of G in random locations;
 * repeat N times
 *     calculate the force on each vertex;
 *     move the vertex c4
 * draw graph on canvas, plotter or any drawing tool.
 * 

munificent

#include <time.h> //  Robert Nystrom
#include <stdio.h> // @munificentbob
#include <stdlib.h> //     for Ginny
#define  r return    //    2008-2019
#define  l(a, b, c, d) for (i y=a;y\
<b; y++) for (int x = c; x < d; x++)
typedef int i;const i H=40;const i W
=80;i m[40][80];i g(i x){r rand()%x;
}void cave(i s){i w=g(10)+5;i h=g(6)
+3;i t=g(W-w-2)+1;i u=g(H-h-2)+1;l(u

krrr

#,::
  AdjustScreenBrightness(-3)
  Return
  
#.::
  AdjustScreenBrightness(3)
  Return
  
AdjustScreenBrightness(step) {
    service := "winmgmts:{impersonationLevel=impersonate}!\\.\root\WMI"