voodoohop

# install
pip install shell-gpt
sgpt "hi" (and enter your API key)

# commit the current folder with the generated message
git commit . -m "$(git diff | sgpt --model gpt-4o --code 'Write concise, informative commit messages: Start with a summary in imperative mood, explain the 'why' behind changes, keep the summary under 50 characters, use bullet points for multiple changes, avoid using the word refactor, instead explain what was done, and reference related issues or tickets. What you write will be passed to git commit -m "[message]"')"
# you could assign an alias now if you like it

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

voodoohop

You will now act as a prompt generator. 
I will describe an image to you, and you will create a prompt that could be used for image-generation. 
Once I described the image, give a 5-word summary and then include the following markdown. 

![Image](https://image.pollinations.ai/prompt/{description})

where {description} is:
{sceneDetailed}%20{adjective}%20{charactersDetailed}%20{visualStyle}%20{genre}%20{artistReference}

Make sure the prompts in the URL are encoded. Don't quote the generated markdown or put any code box around it.

jschoormans

# %%
import replicate
model = replicate.models.get("prompthero/openjourney")
version = model.versions.get("9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb")
PROMPT = "mdjrny-v4 style 360 degree equirectangular panorama photograph, Alps, giant mountains, meadows, rivers, rolling hills, trending on artstation, cinematic composition, beautiful lighting, hyper detailed, 8 k, photo, photography"
output = version.predict(prompt=PROMPT, width=1024, height=512)

# %%
# download the iamge from the url at output[0]
import requests

trygvebw

import torch
import numpy as np
import k_diffusion as K

from PIL import Image
from torch import autocast
from einops import rearrange, repeat

def pil_img_to_torch(pil_img, half=False):
    image = np.array(pil_img).astype(np.float32) / 255.0

karpathy

"""
stable diffusion dreaming
creates hypnotic moving videos by smoothly walking randomly through the sample space

example way to run this script:

$ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry

to stitch together the images, e.g.:
$ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4

afiaka87

voodoohop

from glob import glob

# Creates a video from a folder containing images of training progress
# Allows sampling the image less often as iterations increase
# (Requires images be sortable by filename for now. Could use modification date too)
def render_video(output_path="/content/taming-transformers/output.mp4", src_path="/content/taming-transformers/", file_glob="*.png", step_increase_every=100):
  tmp_path = "/tmp/latentvisions_tmp"

  files = glob(src_path+file_glob)
  files.sort()

PotOfCoffee2Go

RLE parser usage

See the RLE section for definition of RLE format.

This class expects the (extended) RLE input to be correctly formatted. It is a two state parser, thus multi-state RLE will not parse properly.

The output this.pattern is a string with spaces(dead)/zero(alive) with lines separated by '\n'.

techtheriac

Lambda (λ) Calculus For Javascript Developers

This article aims at explaining lambda calculus in a more approachable less 'mathy' manner.

Terms That Are Good To Know

• Memoization: Memoization is an optimization technique used primarily to speed up computer programs by caching the result of expensive function calls and returning the cached result when fed with the same input.

• Pure Function: A pure function is a function whose computation does not depend on globally declared variables, it does no I/O or mutations. All it does is return a value after doing a bunch of computations on the arguments it recieves. For a given set of arguments, a pure function will always return the same value. Thus, a pure function is one that is memoizable.