Glavin001

import base64
from io import BytesIO

from PIL import Image
import torch
import torch.nn as nn
import ollama
from diffusers import DiffusionPipeline, StableDiffusionPipeline
from safetensors.torch import load_file
from transformers import CLIPProcessor, CLIPModel, AutoImageProcessor, AutoModel

Glavin001

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 and experience preferred (super rare at this point).

My own notes from a few months back.

Background

• Survey of LLMS
• Self-attention and transformer networks

Glavin001

If you're using a high-end bluetooth headset on your Macbook Pro it's likely your mac is using an audio codec which favors battery efficiency over high quality. This results in a drastic degradation of sound, the SBC codec is the likely culprit, read more about it here.

Find out what codec you're using

1. Play a song on your headphones
2. Option (⌥) click the Bluetooth button at the top of your screen
3. If you're using AAC or aptX, you can stop here—those are the highest quality codecs.

Change your codec to AAC or aptX

Glavin001

Here are several different ways to test a TCP port without telnet.

BASH (man page)

$ cat < /dev/tcp/127.0.0.1/22
SSH-2.0-OpenSSH_5.3
^C

$ cat &lt; /dev/tcp/127.0.0.1/23

Glavin001

class Frame extends Component {
  componentDidMount() {
    this.iframeHead = this.node.contentDocument.head
    this.iframeRoot = this.node.contentDocument.body
    this.forceUpdate()
  }

  render() {
    const { children, head, ...rest } = this.props
    return (

Glavin001

<?php

require_once __DIR__.'/../vendor/autoload.php';

use Symfony\Component\Console\Helper\Table;
use Symfony\Component\Console\Output\ConsoleOutput;
use Symfony\Component\Console\Output\OutputInterface;
use Silex\Application;

/**

Glavin001

function install_faiss() {
  pyenv shell rise
  pip install numpy
  sudo apt-get install -y \
    build-essential \
    libopenblas-dev \
    gfortran \
    swig

  PREFIX_MAIN=$(pyenv virtualenv-prefix)

Glavin001

import string
import re
import nltk

def normalize(text):
	# remove punctuation
	text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
	# split into words
	# nltk.data.path.append("/nltk_data")
	from nltk.tokenize import word_tokenize

Glavin001

import string
import re
import nltk

def normalize(text):
	# remove punctuation
	text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
	# split into words
	# nltk.data.path.append("/nltk_data")
	from nltk.tokenize import word_tokenize

Glavin001

Github Events Analysis with Neo4j

On July 22, Github announced the 3rd Annual Github Data Challenge presenting multiple sources of data available.

This sounded to me a good opportunity to use their available data and import it in Neo4j in order to have a lot of fun at analyzing the data that fits naturally in a graph.

As I work mainly offline or behind military proxies that do not permit me to use the ReST API, I decided to go for the Github Archive available here, you can then download json files representing Github Events on a daily/hour basis.