visfleet

require 'benchmark'
require 'yaml'

def encode(msg, format)
  case format
  when :yaml
    str = msg.to_yaml
  when :binary
    str = Marshal.dump(msg)
  end

aishfenton

require 'rubygems' 
require "builder"
require "benchmark"
require 'nokogiri' 
require 'erb' 
require 'erubis'

ITERATIONS = 1_000

ERB_TEMPLATE = <<-EOL

mblondel

"""
(C) Mathieu Blondel - 2010
License: BSD 3 clause

Implementation of the collapsed Gibbs sampler for
Latent Dirichlet Allocation, as described in

Finding scientifc topics (Griffiths and Steyvers)
"""

zentrope

// Just an example of some code I ended up not using, but which is kinda neat.

object Utils {

  def linesFromFile(file: File) = {
    io.Source.fromInputStream(getMemoryMappedFileInputStream(file)).getLines
  }

  def getMemoryMappedFileInputStream(file: File): InputStream = {

stober

#!/usr/bin/python
"""
Author: Jeremy M. Stober
Program: GP.PY
Date: Thursday, July 17 2008
Description: Example of Gaussian Process Regression.
"""

from numpy import *
import pylab

non

object Transducer {
  type RF[R, A] = (R, A) => R
  def apply[A, B](f: A => B) =
    new Transducer[B, A] {
      def apply[R](rf: RF[R, B]) = (r, a) => rf(r, f(a))
    }
}

import Transducer.RF

mmozeiko

#include <stdint.h>
#include <stdio.h>
#include <string.h>

#include <xmmintrin.h> // SSE
#include <immintrin.h> // AVX

#ifdef _WIN32

#include <intrin.h> // for __movsb, __movsd, __movsq

non

package half

import scala.math.{pow, round, signum}
import scala.util.Random.nextInt
import java.lang.{Float => JFloat}

/**
 * Float16 represents 16-bit floating-point values.
 *
 * This type does not actually support arithmetic directly. The

pathikrit

Libraries

• Web Server: Play (framework) or http4s (library)
• Actors: akka
• Asynchronous Programming: monix (for tasks, reactors, observables, scheduler etc)
• Authentication: Silhouette
• Authorization: Deadbolt
• Command-line option parsing: case-app
• CSV Parsing: kantan.csv
• DB: doobie (for PostgreSQL)

karpathy

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.