ossa-ma

AI Writing Tropes to Avoid

Add this file to your AI assistant's system prompt or context to help it avoid common AI writing patterns. Source: tropes.fyi by ossama.is

---

Word Choice

"Quietly" and Other Magic Adverbs

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

W4ngatang

''' Script for downloading all GLUE data.

Note: for legal reasons, we are unable to host MRPC.
You can either use the version hosted by the SentEval team, which is already tokenized, 
or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually.
For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example).
You should then rename and place specific files in a folder (see below for an example).

mkdir MRPC
cabextract MSRParaphraseCorpus.msi -d MRPC

AndreFCruz

'''
TicTacToe Game

Console based.
MiniMax AI based on a scoring matrix of the game tree

Python 2.7.9 - 32 bit

@author Andre Cruz
'''

ashishraste

System details : Ubuntu 14.04, python-2.7.

---

1. Download Humor-Sans ttf font from here.
2. Place it in /usr/share/fonts/truetype/. For OSx systems, you'd just need to double-click the font file to install.
3. Run the following commands, to refresh your system's font cache and to delete Maplotlib's font cache.
   

 sudo fc-cache -f -v
 cd ~/.cache/matplotlib; rm fontList.cache; cd #for OSx systems, this file resides in ~/.matplotlib/

cbaziotis

def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':

antonioalmeida

-- Hammerspoon configuration, heavily influenced by sdegutis default configuration

-- init grid
hs.grid.MARGINX = 0
hs.grid.MARGINY = 0
hs.grid.GRIDWIDTH = 8
hs.grid.GRIDHEIGHT = 4

-- disable animation
hs.window.animationDuration = 0

mbollmann

class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one:

karpathy

"""
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)

Jaza

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