- 2010: Introductory STL (10 parts)
- microsoft/nurikabe: I open-sourced and slightly modernized my Nurikabe puzzle solver.
- 2011: Advanced STL: Part 1, Part 2, Part 3, Part 4, Part 5, Part 6
- These videos weren't migrated
import { NodeRuntime } from "@effect/platform-node"; | |
import { Schema } from "@effect/schema"; | |
import { SqsLive, SqsService } from "@repo/shared/src/Sqs"; | |
import { Chunk, Console, Effect, Option, Stream, pipe } from "effect"; | |
import { logError, logInfo } from "effect/Effect"; | |
import { Consumer } from "sqs-consumer"; | |
function makeStream<T, U>({ | |
queueName, |
Let's say contributor
has submitted a pull request to your (author
) project (repo
). They have made changes on their
branch feature
and have proposed to merge this into origin/master
, where
origin -> https://github.com/author/repo.git
Now say you would like to make commits to their PR and push those changes. First, add their fork as a remote called
''' | |
Copyright (C) 2018 by Daniel Foose | |
Permission to use, copy, modify, and/or distribute this software for any purpose with or without fee is hereby granted. | |
THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE | |
''' | |
import re | |
import docker |
# ==Prereq== | |
# Download from app store: | |
# https://apps.apple.com/us/app/macos-mojave/id1398502828?mt=12 | |
# Output file: | |
# /Applications/Install\ macOS\ Mojave.app | |
# On a Mac host, install macinbox | |
# This is if using VirtualBox for virtualization | |
# https://github.com/bacongravy/macinbox | |
sudo gem install macinbox |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
def lengths_to_mask(lengths, max_len=None, dtype=None): | |
""" | |
Converts a "lengths" tensor to its binary mask representation. | |
Based on: https://discuss.pytorch.org/t/how-to-generate-variable-length-mask/23397 |
RAR registration data | |
WinRAR | |
Unlimited Company License | |
UID=4b914fb772c8376bf571 | |
6412212250f5711ad072cf351cfa39e2851192daf8a362681bbb1d | |
cd48da1d14d995f0bbf960fce6cb5ffde62890079861be57638717 | |
7131ced835ed65cc743d9777f2ea71a8e32c7e593cf66794343565 | |
b41bcf56929486b8bcdac33d50ecf773996052598f1f556defffbd | |
982fbe71e93df6b6346c37a3890f3c7edc65d7f5455470d13d1190 | |
6e6fb824bcf25f155547b5fc41901ad58c0992f570be1cf5608ba9 |