How to use:
./wordle.sh
Or try the unlimit mode:
I've been working with Apache Kafka for over 7 years. I inevitably find myself doing the same set of activities while I'm developing or working with someone else's system. Here's a set of Kafka productivity hacks for doing a few things way faster than you're probably doing them now. 🔥
rebase
vs merge
).rebase
vs merge
)reset
vs checkout
vs revert
)git rev-parse
)pull
vs fetch
)stash
vs branch
)reset
vs checkout
vs revert
)# Import Slack Client | |
from slackclient import SlackClient | |
## Import Time | |
import time | |
#Set Bot Name | |
BOT_NAME = '' | |
#We will give the token in a moment |
#!/usr/bin/env python | |
############################################################################### | |
# This script downloads an object from AWS S3. If the object was encrypted, | |
# it will be decrypted on the client side using KMS envelope encryption. | |
# | |
# Envelope encryption fetches a data key from KMS and uses it to encrypt the | |
# file. The encrypted file is uploaded to an S3 bucket along with an encrypted | |
# version of the data key (it's encrypted with a KMS master key). You must | |
# have access to the KMS master key to decrypt the data key and file. To | |
# decrypt, the file is downloaded to the client, the encrypted data key is |
This is free and unencumbered software released into the public domain.
Anyone is free to copy, modify, publish, use, compile, sell, or distribute this software, either in source code form or as a compiled binary, for any purpose, commercial or non-commercial, and by any means.
In jurisdictions that recognize copyright laws, the author or authors of this software dedicate any and all copyright interest in the software to the public domain. We make this dedication for the benefit
FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.
""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
""" | |
This is a batched LSTM forward and backward pass | |
""" | |
import numpy as np | |
import code | |
class LSTM: | |
@staticmethod | |
def init(input_size, hidden_size, fancy_forget_bias_init = 3): |