willurd

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

transitive-bullshit

// total number of samples at each fragment
#define NUM_SAMPLES           {{ numSamples }}

#define NUM_SPIRAL_TURNS      {{ numSpiralTurns }}

#define USE_ACTUAL_NORMALS    {{ useActualNormals }}

#define VARIATION             {{ variation }}

uniform sampler2D sGBuffer;

timvisee

Falsehoods programmers believe about time

This is a compiled list of falsehoods programmers tend to believe about working with time.

Don't re-invent a date time library yourself. If you think you understand everything about time, you're probably doing it wrong.

Falsehoods

• There are always 24 hours in a day.
• February is always 28 days long.
• Any 24-hour period will always begin and end in the same day (or week, or month).

nicebyte

#pragma once

#define DYN_ARR_OF(type) struct  { \
  type *data; \
  type *endptr; \
  uint32_t capacity; \
}

#if !defined(__cplusplus)
#define decltype(x) void*

JoeyBurzynski

58 bytes of CSS to look great nearly everywhere

When making this website, i wanted a simple, reasonable way to make it look good on most displays. Not counting any minimization techniques, the following 58 bytes worked well for me:

main {
  max-width: 38rem;
  padding: 2rem;
  margin: auto;
}

boyter

// Primitive hash function that for a string returns a positive 32 bit int
// Do not use in production, use murmur3 or fnv1
// You can improve this by changing 5 to 31
Object.defineProperty(String.prototype, 'hashCode', {
  value: function() {
    var hash = 0, i, chr;
    for (i = 0; i < this.length; i++) {
      chr   = this.charCodeAt(i);
      hash  = ((hash << 5) - hash) + chr;
      hash |= 0; // Convert to 32bit integer

bitonic

// Copyright (c) 2021 Francesco Mazzoli <[email protected]>
//
// Permission to use, copy, modify, and distribute this software for any
// purpose with or without fee is hereby granted, provided that the above
// copyright notice and this permission notice appear in all copies.
//
// THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
// WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
// MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
// ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES

Hellisotherpeople

You probably don't know how to do Prompt Engineering

(This post could also be titled "Features missing from most LLM front-ends that should exist")

Apologies for the snarky title, but there has been a huge amount of discussion around so called "Prompt Engineering" these past few months on all kinds of platforms. Much of it is coming from individuals who are peddling around an awful lot of "Prompting" and very little "Engineering".

Most of these discussions are little more than users finding that writing more creative and complicated prompts can help them solve a task that a more simple prompt was unable to help with. I claim this is not Prompt Engineering. This is not to say that crafting good prompts is not a difficult task, but it does not involve doing any kind of sophisticated modifications to general "template" of a prompt.

Others, who I think do deserve to call themselves "Prompt Engineers" (and an awful lot more than that), have been writing about and utilizing the rich new eco-system

ruvnet

#             - Q* (Q-Star)
#    /\__/\   - q.py
#   ( o.o  )  - v0.0.1
#     >^<     - by @rUv

# 01110010 01110101 01110110
# This is a proof of concept implementation of the Q* (AGI) leak from OpenAi
# This Python code defines a sophisticated Q-learning agent for reinforcement learning. 
# It includes dynamic exploration, learning from experiences, and checks for convergence. 
# The agent's capabilities are refined iteratively to optimize its decision-making strategy in a given environment.

rolux

# This generates, along with the init latent, a second noise latent.
# Each of these has 16 channels, and the animation below has 16 phases.
# During phase i, the amount of noise in channel i decreases from max to
# 0, while the amount of noise in channel i + 1 increases from 0 to max.
# 
# For a render function and video export, see
# https://github.com/rolux/flux-random-walk

prompt = "a horse riding an astronaut"
width, height = 1024, 1024