qpwo

# unfortunately, the stdinready event fires all the time, not just when repl ready.
# Based on https://gist.github.com/andy0130tw/39472331530d1a0e25459a547ed2c9d5

import io
import selectors
from selectors import EVENT_READ, EVENT_WRITE
import subprocess
from typing import cast

sel = selectors.DefaultSelector()

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sudo apt install -y nftables

sudo systemctl restart  nftables.service

# https://mullvad.net/en/help/split-tunneling-with-linux-advanced
echo '
table inet excludeTraffic {
  chain allowIncoming {
    type filter hook input priority -100; policy accept;

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import os
from pathlib import Path
import json
import shutil
# 192.168.122.#{i+150}
template = """
Vagrant.configure("2") do |config|
    config.vm.box = "debian/bookworm64"

    # provider config

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Step 1: install the multicommand extension: ryuta46.multi-command https://marketplace.visualstudio.com/items?itemName=ryuta46.multi-command

Step 2: add to settings.json:

  "multiCommand.commands": [
    {
      "command": "multiCommand.runInBothTerminals",
      "interval": 30,
 "sequence": [

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// full screen thread
let el = document.querySelector(".p-view_contents--secondary")
el.style.minWidth = '100vw';
el.style.minHeight = '100vh';
el.style.position = 'fixed';
el.style.top = '0';
el.style.left = '0';
el.style.margin = '0';
el.style.padding = '0';
el.style.overflow = 'auto';

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name                                             	numel         	dtype          	shape           	path
lm_head.weight                                   	2_101_346_304 	torch.bfloat16 	(128256, 16384) 	model-00191-of-00191.safetensors
model.embed_tokens.weight                        	2_101_346_304 	torch.bfloat16 	(128256, 16384) 	model-00001-of-00191.safetensors
model.layers.0.input_layernorm.weight            	16_384        	torch.bfloat16 	(16384,)        	model-00003-of-00191.safetensors
model.layers.0.mlp.down_proj.weight              	872_415_232   	torch.bfloat16 	(16384, 53248)  	model-00003-of-00191.safetensors
model.layers.0.mlp.gate_proj.weight              	872_415_232   	torch.bfloat16 	(53248, 16384)  	model-00002-of-00191.safetensors
model.layers.0.mlp.up_proj.weight                	872_415_232   	torch.bfloat16 	(53248, 16384)  	model-00002-of-00191.safetensors
model.layers.0.post_attention_layernorm.weight   	16_384        	torch.bfloat16 	(16384,)        	model-00003-of-00191.safetensors
mode

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import asyncio
import json
import os
from random import shuffle
import re
import unicodedata

from anthropic import AsyncAnthropic
import dotenv
import spotipy

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#!/usr/bin/env python

# open lxterminal app from app launcher
# python checkinscript.py

import glob
import json
import os
from pdb import run
import time

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#!/usr/bin/bash

# copy into /usr/bin or whatever and chmod+x

# example:
# $ gpt 'untar and ensure subdir (but no extraneous nesting)'
# ```bash mkdir subdir && tar -xvf archive.tar -C subdir --strip-components=1 ```


# https://platform.openai.com/api-keys

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# Neural net implementation in PyTorch without autograd
# https://gist.github.com/qpwo/86c41eb6869b36dc53d5df798e66a040
# based on https://gist.github.com/jessicabuzzelli/8317c2350ce63b899e0682ce256c44cf#file-image_classification_nn-py
# wget https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz

import numpy as np
import torch as torch
import gzip
import pickle
# from tqdm import tqdm_notebook as tqdm