sloev

from multiprocessing.dummy import Pool

import subprocess
import tempfile
import logging
import time
import random
import os
from base64 import urlsafe_b64encode
import uuid

sloev

debug psql statements

when you get stuff like this:

5 UTC [1] LOG:  database system is ready to accept connections
db_1                | 2021-11-24 09:58:49.133 UTC [83] ERROR:  invalid input value for enum task_status: "ready" at character 339
db_1                | 2021-11-24 09:58:49.133 UTC [83] STATEMENT:  BEGIN;

its nice to be able to quickly figure out where the error is in the input sql file.

sloev

#!/bin/bash

# append-only kv-store using jq as querying mechanism
# author: sloev.github.io


# declare kv store as string
kv_store=""

# append some json to 

sloev

curl 'https://gist.githubusercontent.com/sloev/5de3ed6bc5d3e78f656029071eb16409/raw/728aee9e9a88281d7bcb398f2408aa5051632c1d/gpslog.py' > ~/gpslog.py 
    
curl 'https://gist.githubusercontent.com/sloev/5de3ed6bc5d3e78f656029071eb16409/raw/728aee9e9a88281d7bcb398f2408aa5051632c1d/gpslog.sh' > ~/gpslog.sh 
    
curl 'https://gist.githubusercontent.com/sloev/5de3ed6bc5d3e78f656029071eb16409/raw/728aee9e9a88281d7bcb398f2408aa5051632c1d/gpslog.service' > ~/gpslog.service

sudo cp ~/gpslog.service /etc/systemd/system/gpslog.service
sudo systemctl enable gpslog.service

sloev

package hub

import (
	"bytes"
	"log"
	"net/http"
	"sync"
	"time"

	"github.com/gorilla/websocket"

sloev

-- create new images
vPlayer1 = of.VideoPlayer()
outputFbo = of.Fbo()

serverTemp = syphon.Server()
serverOutput = syphon.Server()

outputWidth = 1280
outputHeight = 720
previewWidth = 400

sloev

extends TextureRect


var shader_material
var screen_size

func _ready():
	screen_size = OS.get_screen_size()
	shader_material = self.material
	set_process_input(true)

sloev

import pickle
import json
import tensorflow as tf


from keras.models import load_model
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense, Embedding, LSTM
import tensorflow as tf

sloev

1. create repo

2. install https://gitjournal.io/ on phone

3. setup gitjournal on phone

4. clone repo on computer

5. install script alias

sloev

(async () => {
    const GUN = require('gun');
    const dbName = "database" + new Date()
    require('gun/lib/path.js')
    var SEA = GUN.SEA;
    var gun = GUN()

    // 1......
    // create server certificate for database (allowing server to delete all user objects)
    var ServerAdmin = await SEA.pair()