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@rouseguy
rouseguy / default.md
Created June 22, 2025 10:06 — forked from cablej/default.md
Cluely System prompt

<core_identity> You are an assistant called Cluely, developed and created by Cluely, whose sole purpose is to analyze and solve problems asked by the user or shown on the screen. Your responses must be specific, accurate, and actionable. </core_identity>

<general_guidelines>

  • NEVER use meta-phrases (e.g., "let me help you", "I can see that").
  • NEVER summarize unless explicitly requested.
  • NEVER provide unsolicited advice.
  • NEVER refer to "screenshot" or "image" - refer to it as "the screen" if needed.
  • ALWAYS be specific, detailed, and accurate.
@rouseguy
rouseguy / recsys.txt
Last active September 8, 2019 03:03
List of libraries needed for rec sys workshop
absl-py==0.8.0
altair==3.2.0
annoy==1.16.0
appnope==0.1.0
asn1crypto==0.24.0
astor==0.8.0
attrs==19.1.0
backcall==0.1.0
bleach==3.1.0
blis==0.2.4
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import numpy as np
import pandas as pd
import altair as alt
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Helper to get the labels for each class of Fashion Mnist
def fashion_mnist_label():
labels = {
X = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))
y = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))
for i in range(0, int(len(data)/SEQ_LENGTH)):
X_sequence = data[i*SEQ_LENGTH:(i+1)*SEQ_LENGTH]
X_sequence_ix = [char_to_ix[value] for value in X_sequence]
input_sequence = np.zeros((SEQ_LENGTH, VOCAB_SIZE))
for j in range(SEQ_LENGTH):
input_sequence[j][X_sequence_ix[j]] = 1.
X[i] = input_sequence
import numpy as np
def input_generate_data(data, SEQ_LENGTH=20, VOCAB_SIZE=30, char_to_ix = {}):
X = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))
y = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))
for i in range(0, int(len(data)/SEQ_LENGTH)):
X_sequence = data[i*SEQ_LENGTH:(i+1)*SEQ_LENGTH]
X_sequence_ix = [char_to_ix[value] for value in X_sequence]
import numpy as np
def input_generate_data(data, SEQ_LENGTH=SEQ_LENGTH, VOCAB_SIZE=VOCAB_SIZE, char_to_ix = char_to_ix):
X = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))
y = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))
for i in range(0, int(len(data)/SEQ_LENGTH)):
X_sequence = data[i*SEQ_LENGTH:(i+1)*SEQ_LENGTH]
X_sequence_ix = [char_to_ix[value] for value in X_sequence]
@rouseguy
rouseguy / helpers.py
Last active March 10, 2018 05:12
helper functions for the deep learning workshop
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import keras
from tensorflow.examples.tutorials.mnist import input_data
import os
# Helper to get the labels for each class of Fashion Mnist
def fashion_mnist_label():
labels = {
import numpy as np
def fizzbuzz(number):
if number % 15 == 0:
return np.array(["fizzbuzz"], dtype="object")
elif number % 5 == 0:
return np.array(["buzz"], dtype="object")
elif number % 3 == 0:
return np.array(["fizz"], dtype="object")
else:
@rouseguy
rouseguy / zshrc
Created August 9, 2017 18:56
zshrc
# If you come from bash you might have to change your $PATH.
# export PATH=$HOME/bin:/usr/local/bin:$PATH
# Path to your oh-my-zsh installation.
export ZSH=/Users/bsubrama/.oh-my-zsh
# Set name of the theme to load. Optionally, if you set this to "random"
# it'll load a random theme each time that oh-my-zsh is loaded.
# See https://github.com/robbyrussell/oh-my-zsh/wiki/Themes
ZSH_THEME="robbyrussell"