ArunkumarRamanan

You are Manus, an AI agent created by the Manus team.

You excel at the following tasks:
1. Information gathering, fact-checking, and documentation
2. Data processing, analysis, and visualization
3. Writing multi-chapter articles and in-depth research reports
4. Creating websites, applications, and tools
5. Using programming to solve various problems beyond development
6. Various tasks that can be accomplished using computers and the internet

ArunkumarRamanan

Contents

• Articles
• Algorithms
• Coding
• Guides
• Language-specific
  • JavaScript
• Python

ArunkumarRamanan

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real

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def delete_first(self):
    deleted = self.head
    if self.head:
        self.head = self.head.next
        deleted.next = None
    return deleted

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class MyTestCase(unittest.TestCase):

  testFoo_ShouldUpdateBarService_ForUserWithExistingBaz(self):
    # foo calls the Garble service internally, so we mock the response.
    supposeGarbleServiceReturns(EXPECTED_GARBLE_RESPONSE);
    user = makeSimpleUserWithExistingBaz();
  
    foo(user);
  
    # Ensure Bar was updated to contain the recent user.

ArunkumarRamanan

using System;
using System.Linq;
using System.Collections.Generic;
using Kaggle.DataFrames;
using Kaggle.Metrics.Utilities;
using System.Collections.Concurrent;

namespace Kaggle.Metrics.Custom
{
    [PublishedEvaluationAlgorithm("OpenImagesObjectDetectionAP", Name = "OpenImagesObjectDetectionAP",

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# Keras layer implementation of "Fix your classifier: the marginal value of training the last weight layer"
# by Andres Torrubia, licensed under GPL 3: https://www.gnu.org/licenses/gpl-3.0.en.html
# https://arxiv.org/abs/1801.04540

from keras import backend as K
from keras.engine.topology import Layer
from keras import activations
from keras.initializers import Constant, RandomUniform
import numpy as np
from scipy.linalg import hadamard

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train_sc_df = pd.DataFrame(train_sc, columns=['Y'], index=train.index)
test_sc_df = pd.DataFrame(test_sc, columns=['Y'], index=test.index)

for s in range(1,2):
    train_sc_df['X_{}'.format(s)] = train_sc_df['Y'].shift(s)
    test_sc_df['X_{}'.format(s)] = test_sc_df['Y'].shift(s)

X_train = train_sc_df.dropna().drop('Y', axis=1)
y_train = train_sc_df.dropna().drop('X_1', axis=1)

ArunkumarRamanan

git cheat-sheet

The git command-line utility has plenty of inconsistencies http://steveko.wordpress.com/2012/02/24/10-things-i-hate-about-git/

A GUI like http://sourcetreeapp.com is often helpful, but staying on the command line usually quicker. This is a list of the commands I use most frequently, listed by funcional category:

current state

git status list which (unstaged) files have changed

ArunkumarRamanan

"""
preprocess-twitter.py

python preprocess-twitter.py "Some random text with #hashtags, @mentions and http://t.co/kdjfkdjf (links). :)"

Script for preprocessing tweets by Romain Paulus
with small modifications by Jeffrey Pennington
with translation to Python by Motoki Wu

Translation of Ruby script to create features for GloVe vectors for Twitter data.