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"""
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

duydo

# Use this script to test that your Telegram bot works.
#
# Install the dependency
#
#   $ gem install telegram_bot
#
# Run the bot
#
#   $ ruby bot.rb
#

duydo

Decoding the data in /proc/net/tcp:

Linux 5.x  /proc/net/tcp
Linux 6.x  /proc/PID/net/tcp

Given a socket:

$ ls -l  /proc/24784/fd/11
lrwx------ 1 jkstill dba 64 Dec  4 16:22 /proc/24784/fd/11 -> socket:[15907701]

duydo

##################################################################
# /etc/elasticsearch/elasticsearch.yml
#
# Base configuration for a write heavy cluster
#

# Cluster / Node Basics
cluster.name: logng

# Node can have abritrary attributes we can use for routing

duydo

import operator
from pprint import pprint

def is_dict(d):
    return isinstance(d, dict)
    
def get(c, k, default=None):
    try:
        return c[k]
    except (IndexError, KeyError, TypeError):

duydo

nginx/
!nginx/.gitkeep
!nginx/logs/.gitkeep

src/

tmp/

duydo

the
of
to
and
a
in
is
it
you
that

duydo

#System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

##Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

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1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

duydo

import sys
from gevent import server
from gevent.baseserver import _tcp_listener
from gevent.monkey import patch_all; patch_all()
from multiprocessing import Process, current_process, cpu_count


def note(format, *args):
    sys.stderr.write('[%s]\t%s\n' % (current_process().name, format%args))