Muqito

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

Muqito

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

Muqito

"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

Muqito

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Muqito

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

Muqito

SVT-AV1 is the fastest multithreaded AV1 encoder
You can download up to date BtbN ffmpeg build with latest STV-AV1 code.

Aom-av1 is the official av1 decoder/encoder but it's the slowest and lacks of a real massive multithreading.
Rav1e is a xiph encoder project. It's also very slow but you can use multithreading with tiles.
Dav1d is a veryfast AV1 VLC decoder project.

Svt-av1 is an heavy multithreading intel decoder and encoder. It produce good quality but bigger file size.
It can produce 8 and 10 bit video with CRF (default), CQP, VBR and CBR (test).
Two pass are also available with standalone SvtAv1EncApp but not in ffmpeg.

Muqito

// ref: https://ferrous-systems.com/blog/rustls-borrow-checker-p1/
#[derive(Debug)]
struct Reader<'a> {
    buffer: &'a mut [u8]
}
impl<'a> Reader<'a> {
    pub fn new(buffer: &'a mut [u8]) -> Self {
        Self {
            buffer,
        }

Muqito

#!/bin/bash -e

sudo apt install \
  curl \
  build-essential \
  libpcre3-dev \
  libssl-dev \
  zlib1g-dev \
  libxml2-dev \
  libxslt1-dev \

Muqito

name: Security audit
on:
  push:
    paths:
      - '**/Cargo.toml'
      - '**/Cargo.lock'
jobs:
  security_audit:
    runs-on: ubuntu-latest
    steps:

Muqito

// Async await func 
async function getTimelineData() {
 var response = await fetch('/some-api-url')
  return response.json()
}

async function populateTimelineInit() {

  var data = await getTimelineData();
  new vis.DataSet(data);