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package main

import (
	"context"
	"errors"
	"fmt"
	"io"
	"log"
	"os"
	"runtime"

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import cv2

def add_gaussian_noise(X_imgs):
    gaussian_noise_imgs = []
    row, col, _ = X_imgs[0].shape
    # Gaussian distribution parameters
    mean = 0
    var = 0.1
    sigma = var ** 0.5
    

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/** getSpread returns the difference between the highest bid and the lowest
 * offer (ask). It throws an exception, if the orders do not contain both bids
 * and asks. */
double OrderBook::getSpread(std::vector<OrderBookEntry>& orders)
{
    if (orders.empty()) {
        return 0;
    }
    // https://web.stanford.edu/class/cme241/lecture_slides/Tour-OrderBook.pdf#page=3

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go.log

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# SGR color constants
# rene-d 2018

class Colors:
    """ ANSI color codes """
    BLACK = "\033[0;30m"
    RED = "\033[0;31m"
    GREEN = "\033[0;32m"
    BROWN = "\033[0;33m"
    BLUE = "\033[0;34m"

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Learning Python 3

This gist provides the learning-python3.ipynb notebook file, which can be viewed and edited in a Jupyter Notebook server to learn Python 3.

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# Reference: kstathou/acl-search-engine

# !pip install faiss-cpu --no-cache
# !pip install sentence_transformers

import faiss
import numpy as np
import pandas as pd 
import pickle
import torch

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llmc() {
    local system_prompt='Output a command that I can run in a ZSH terminal on macOS to accomplish the following task. Try to make the command self-documenting, using the long version of flags where possible. Output the command first enclosed in a "```zsh" codeblock followed by a concise explanation of how it accomplishes it.'
    local temp_file=$(mktemp)
    local capturing=true
    local command_buffer=""
    local first_line=true
    local cleaned_up=false # Flag to indicate whether cleanup has been run

    cleanup() {
        # Only run cleanup if it hasn't been done yet

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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

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FROM docker.io/python:3.9-bullseye AS build
WORKDIR "/app"

# Install dependecies
# hadolint ignore=DL3008,DL3013
RUN set -eux && \
    apt-get update; \
    apt-get install --no-install-recommends -y \
        python3-dev build-essential patchelf upx; \
    apt-get clean; \