mitchellh

I get asked pretty regularly what my opinion is on merge commits vs rebasing vs squashing. I've typed up this response so many times that I've decided to just put it in a gist so I can reference it whenever it comes up again.

I use merge, squash, rebase all situationally. I believe they all have their merits but their usage depends on the context. I think anyone who says any particular strategy is the right answer 100% of the time is wrong, but I think there is considerable acceptable leeway in when you use each. What follows is my personal and professional opinion:

Piinks

// Copyright (c) 2019, the Dart project authors.  Please see the AUTHORS file
// for details. All rights reserved. Use of this source code is governed by a
// BSD-style license that can be found in the LICENSE file.

import 'dart:math' as math;

import 'package:flutter/foundation.dart';
import 'package:flutter/gestures.dart';
import 'package:flutter/material.dart';
import 'package:flutter/rendering.dart';

doraTeX

#!/bin/bash
SCRIPTNAME=$(basename "$0")

function realpath () {
  f=$@;
  if [ -d "$f" ]; then
    base="";
    dir="$f";
  else
    base="/$(basename "$f")";

kconner

macOS Internals

Understand your Mac and iPhone more deeply by tracing the evolution of Mac OS X from prelease to Swift. John Siracusa delivers the details.

Starting Points

How to use this gist

You've got two main options:

yoavg

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

rodydavis

import 'dart:async';

import 'package:flutter/material.dart';
import 'package:flutter_riverpod/flutter_riverpod.dart';
import 'package:riverpod_annotation/riverpod_annotation.dart';

part 'generated.g.dart';

@riverpod
class GeneratedWidget extends _$GeneratedWidget {

rbitr

Random numbers in bash et al

There is a variable $RANDOM than you can read in bash or zsh to get a random number beteen 0 and 32767. Here in zsh on my (old) mac:

% echo $RANDOM
13757
% echo $RANDOM
16896

craiglabenz

import 'dart:math';
import 'package:flutter/material.dart';
import 'package:flutter/rendering.dart';

void main() {
  runApp(MyApp());
}

class MyApp extends StatelessWidget {
  @override

MelbourneDeveloper

Test	Time (Microseconds)
ValueNotifier	0.14619362615225495
StateNotifier	0.6242255
Cubit	1.2855485
StreamController	1.3412715

macOS Mac Mini - 3.2 GHz 6-Core Intel Core i7, running under Flutter Release Mode

mala

Meety脆弱性 2022-11

文責 mala

経緯:

• Meety退会しようと思ったがアカウントがなかったので、退会するためにアカウントを作ることにした。

免責:

• 気になった範囲ですぐに見つかったもののうち、悪用可能なものを記載しています。
• 好ましくないが仕様だろうというものは書いていません。