Eugene Melnikov melnikaite

## gist:f0730dc6ade93ec83527
# remove all unused environments from applicationюньд and generate hash
ruby -e 'require "yaml"; puts YAML.load(File.read("config/application.yml"))'

# run script to create yml file
application_yml='{"test"=>{"protocol"=>"https", "host"=>"example.com"}}' ruby -e 'require "yaml"; File.open("config/application.yml", "w") {|f| f.write(eval(ENV["application_yml"]).to_yaml) }'

## bootstrap-fileupload.js
//conflict with ClientSideValidations
//this.$input.attr('name', this.name)

## r.csv

          
            Benchmark
            activerecord-mysql
            activerecord-postgresql
            activerecord-sqlite
            mongoid-mongodb
            sequel-mysql
            sequel-postgresql
            sequel-sqlite

            
              Eager Loading Query Per Association With 1-1 Records: 640 objects 22 times-No Transaction
              5.921149
              6.284935
              5.860397
              1.314835
              0.254055
              0.283381
              0.356529

            
              Eager Loading Query Per Association With 1-1 Records: 640 objects 22 times-Transaction
              6.012105
              6.493957
              5.738628
              
              0.253842
              0.282353
              0.376487

            
              Eager Loading Query Per Association With 1-32 Records: 1024 objects 22 times-No Transaction
              1.087165
              1.114392
              1.037673
              0.908334
              0.190097
              0.206294
              0.273892

            
              Eager Loading Query Per Association With 1-32 Records: 1024 objects 22 times-Transaction
              1.085952
              1.150046
              1.015849
              
              0.191712
              0.212131
              0.271282

            
              Eager Loading Query Per Association With 1-32-32 Records: 2048 objects 9 times-No Transaction
              0.848494
              0.894651
              0.810860
              0.082494
              0.168703
              0.170906
              0.239950

            
              Eager Loading Query Per Association With 1-32-32 Records: 2048 objects 9 times-Transaction
              0.835610
              0.867175
              0.787067
              
              0.168840
              0.171462
              0.

## gist:4191ebf22acf487bcb7c1e53d0012d69
0x63CE9f57E2e4B41d3451DEc20dDB89143fD755bB

## enable.rb
# To enable JSON functions in SQLite run in irb
require 'sqlite3'
c = SQLite3::Database.new('database')
c.enable_load_extension(1)
c.load_extension('/usr/local/Cellar/sqlite/3.14.2/lib/libsqlitefunctions.dylib')

## bls.js
const bls = require('bls-lib');

bls.onModuleInit(() => {
    bls.init();
    const numOfPlayers = 7;
    const threshold = 3;
    const msg = 'hello world';
    console.log({numOfPlayers, threshold, msg});

    // set up master key share

## yandex.radio.bttpreset
{
  "BTTPresetName" : "Default",
  "BTTPresetUUID" : "153FFA02-38FC-4862-B91A-24CB898C9B55",
  "BTTPresetContent" : [
    {
      "BTTAppBundleIdentifier" : "BT.G",
      "BTTAppName" : "Global",
      "BTTAppAutoInvertIcon" : 1,
      "BTTAppSpecificSettings" : {

## assets.rake
# lib/capistrano/tasks/assets.rake

Rake::Task['deploy:assets:precompile'].clear

namespace :deploy do
  namespace :assets do
    desc 'Precompile assets locally and then rsync to remote servers'
    task :precompile do
      local_manifest_path = %x{ls public/assets/manifest*}.strip

## asyncReadableStream.js
const { Readable } = require('stream');

class RandomNumberStream extends Readable {
    constructor(options) {
        super(options);

        this.isTimeToEndIt = false;
        setTimeout(() => {
            this.isTimeToEndIt = true;
        }, 10 * 1000);

## Transparency protocol
I don't understand the purpose of using EVM off-chain or limiting solutions by underground programming languages when it finally ends up with publishing data in cheap storage and a possibility to validate proofs:
- Polygon's solution is limited by 100TPS
- zkSync and StarkWare can achieve 3k TPS but are limited by L1 block size
- even my favorite Avalanche Subnet reports about 4.5k TPS, but also is limited by the vertical scaling potential of validators' hardware

I propose using "Transparency protocol" to achieve the same but in a more flexible, efficient, and scalable way.
You just save for your web2.0 application a changelog with public data, share this data and periodically save Merkle tree to a blockchain. In this case, we are not flooding L1 with application data, we save just small hashes once a day.

In "Transparency protocol" applications share only significant, anonymized pieces of data. Anyone can make sure his data is valid and in place via "read" endpoints. Anyone can make sure that history is va
	# remove all unused environments from applicationюньд and generate hash
	ruby -e 'require "yaml"; puts YAML.load(File.read("config/application.yml"))'

	# run script to create yml file
	application_yml='{"test"=>{"protocol"=>"https", "host"=>"example.com"}}' ruby -e 'require "yaml"; File.open("config/application.yml", "w") {\|f\| f.write(eval(ENV["application_yml"]).to_yaml) }'
	//conflict with ClientSideValidations
	//this.$input.attr('name', this.name)
Benchmark	activerecord-mysql	activerecord-postgresql	activerecord-sqlite	mongoid-mongodb	sequel-mysql	sequel-postgresql	sequel-sqlite
Eager Loading Query Per Association With 1-1 Records: 640 objects 22 times-No Transaction	5.921149	6.284935	5.860397	1.314835	0.254055	0.283381	0.356529
Eager Loading Query Per Association With 1-1 Records: 640 objects 22 times-Transaction	6.012105	6.493957	5.738628		0.253842	0.282353	0.376487
Eager Loading Query Per Association With 1-32 Records: 1024 objects 22 times-No Transaction	1.087165	1.114392	1.037673	0.908334	0.190097	0.206294	0.273892
Eager Loading Query Per Association With 1-32 Records: 1024 objects 22 times-Transaction	1.085952	1.150046	1.015849		0.191712	0.212131	0.271282
Eager Loading Query Per Association With 1-32-32 Records: 2048 objects 9 times-No Transaction	0.848494	0.894651	0.810860	0.082494	0.168703	0.170906	0.239950
Eager Loading Query Per Association With 1-32-32 Records: 2048 objects 9 times-Transaction	0.835610	0.867175	0.787067		0.168840	0.171462	0.
	# To enable JSON functions in SQLite run in irb
	require 'sqlite3'
	c = SQLite3::Database.new('database')
	c.enable_load_extension(1)
	c.load_extension('/usr/local/Cellar/sqlite/3.14.2/lib/libsqlitefunctions.dylib')
	const bls = require('bls-lib');

	bls.onModuleInit(() => {
	bls.init();
	const numOfPlayers = 7;
	const threshold = 3;
	const msg = 'hello world';
	console.log({numOfPlayers, threshold, msg});

	// set up master key share
	{
	"BTTPresetName" : "Default",
	"BTTPresetUUID" : "153FFA02-38FC-4862-B91A-24CB898C9B55",
	"BTTPresetContent" : [
	{
	"BTTAppBundleIdentifier" : "BT.G",
	"BTTAppName" : "Global",
	"BTTAppAutoInvertIcon" : 1,
	"BTTAppSpecificSettings" : {
	# lib/capistrano/tasks/assets.rake

	Rake::Task['deploy:assets:precompile'].clear

	namespace :deploy do
	namespace :assets do
	desc 'Precompile assets locally and then rsync to remote servers'
	task :precompile do
	local_manifest_path = %x{ls public/assets/manifest*}.strip
	const { Readable } = require('stream');

	class RandomNumberStream extends Readable {
	constructor(options) {
	super(options);

	this.isTimeToEndIt = false;
	setTimeout(() => {
	this.isTimeToEndIt = true;
	}, 10 * 1000);
	I don't understand the purpose of using EVM off-chain or limiting solutions by underground programming languages when it finally ends up with publishing data in cheap storage and a possibility to validate proofs:
	- Polygon's solution is limited by 100TPS
	- zkSync and StarkWare can achieve 3k TPS but are limited by L1 block size
	- even my favorite Avalanche Subnet reports about 4.5k TPS, but also is limited by the vertical scaling potential of validators' hardware

	I propose using "Transparency protocol" to achieve the same but in a more flexible, efficient, and scalable way.
	You just save for your web2.0 application a changelog with public data, share this data and periodically save Merkle tree to a blockchain. In this case, we are not flooding L1 with application data, we save just small hashes once a day.

	In "Transparency protocol" applications share only significant, anonymized pieces of data. Anyone can make sure his data is valid and in place via "read" endpoints. Anyone can make sure that history is va