Arpit Soni

Arpit Soni

1648733544

GitHub copilot made dream girlfriend for me

GitHub copilot made dream girlfriend for me

#githubcopilot #copilot #memes #gf

https://www.youtube.com/watch?v=XkiPyVzBvng

GitHub copilot made dream girlfriend for me
黎 飞

黎 飞

1640883600

我在 15 分鐘內發了一個百倍 MEME 幣!

你知道在不懂任何程式碼的情況下,你也可以單憑一己之力創建出類似狗狗幣的迷因幣嗎?🤑

狗狗幣 DOGE、柴犬幣 SHIB、MONA、SAFEMOON 等等,這些都是幣圈裡眾所週知的迷因幣。

某天我不經意地在刷油管時,突然刷到一個教你如何創建迷因幣的教學視頻。

整個創建過程非常簡單,只需要不到 15 分鐘的時間,在沒有任何程式語言的背景下,沒有任何技術團隊,也不需要花很多錢,就這樣生成了我人生中的第一個加密貨幣。🪙

當你成功創建了屬於自己的加密貨幣或迷因幣以後,你可以將這些貨幣運用在自己的社群裡,也可以嘗試通過任何行銷手法,或許能夠讓你的迷因幣變成下一個狗狗幣!🤑

在今天的視頻裡,我除了會手把手教你如何創建屬於自己的迷因幣以外,也會教你如何將這些貨幣添加到平台上,讓大家都可以買賣到你的貨幣!

如果你想知道如何在 15 分鐘內創建自己的迷因幣,記得不要錯過今天的視頻!

影片中提到的創建 MEME 幣鏈接:
1、https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.2+commit.661d1103.js
2、https://github.com/jklepatch/eattheblocks/blob/master/screencast/308-create-bep20-token-bsc/Token.sol

Pheople:https://notconstitutiondao.com/13

⚠️ 重要聲明:今天的視頻僅供參考,並非投資建議。投資有風險,請謹慎投資。

 
時間軸:
00:00  前導
01:18  如何創建 MEME 幣 🪙

 

#memes

我在 15 分鐘內發了一個百倍 MEME 幣!

New Crypto

1629876759

Baby Panda Inu Overview

What is Baby Panda Inu?

Baby Panda Inu is a decentralized finance platform built on BSC (Binance Smart Chain), focusing on 2 main platforms: DEX - Decentralized exchange that allows users to farm LPs tokens, stake, swap, earn/win tokens and Multichain Wallet - a cross-network payment wallet that offers the convenience of paying and owning tokens across all blockchain networks.

Baby Panda Inu will help drive the development of decentralized finance in a new and innovative way, providing a comprehensive experience for global crypto users, contributing to building a positive DeFi community that inspires a bright future where everything is optimized on all transactions by the development policy of decentralized finance.

#BabyPandaINU 
#BabyPandaINUBSC
#BABYPANDA 
#BABYPANDAINUNEP20 
#BABYPANADADEFI
#cryptocurrency 
#memes

Baby Panda Inu Overview

20 More JavaScript Memes

The last one was a hit, so here’s 20 more JavaScript memes!

1. This one is for Junior JS Devs

2. Full handshake

3. Because JS needs to be special

4. Is it alive or not?

5. Which one are you?

6. var = var

7. Oh… The classic meme

8. I don’t know .this either

9. TS ❤

10. We all are scared!

11. Not gonna lie, not the furthest from the truth.

12. How many JS Devs are needed to change a light bulb?

13. Pure evil

14. JavaScript is like Java but with the Script on the end

15. jQuery is the answer

16. I am you

17. I am unga bunga

18. Maybe too harsh?

19. But when I do I print too big numbers?…

20. And one for the end…

#javascript #memes #programming #coding #humor

20 More JavaScript Memes
Brennan  Veum

Brennan Veum

1615096680

Building a Meme Generator With RedwoodJS — The JavaScript Answer to Rails

When I first heard about RedwoodJS, I thought it was just another front-end JavaScript framework. I wondered whether it would it be like React or more like Angular. As it turned out, I didn’t know what RedwoodJS was, but now I can’t wait to build more projects using it.

What Is RedwoodJS?

RedwoodJS is a full-stack, serverless JavaScript framework. It is the JavaScript answer to Rail’s or Django. It uses the Jamstack approach to build an application with both front-end and back-end code. It also uses popular frameworks and libraries to accomplish this:

  • React
  • GraphQL
  • Prisma

Using RedwoodJS allows you to build full-stack applications quickly. The Redwood CLI generates boilerplate code for anything you need, including scaffolding pages, SDLs, and services based on your data schema. And you really don’t have to think much about the database, so it works well for React developers unfamiliar with back-end code.

To dig into the details of how RedwoodJS works, consider the example of a meme generator. The meme generator allows users to create a meme based on a random image from imgflip.com. First, users will need to register for an account. Then they will add text to the image. When they are done, they can click a button to save the image and store the user email and the image URL in a database. Users will view all submitted memes on another page in the app.

As you can see, RedwoodJS will create almost everything that’s needed for this entire project. You will still need a few additions to your stack to do a complete project. For this example, we’ll use Netlify for simple user authentication. Heroku can quickly spin up and host a PostgreSQL instance to store the user data, and Cloudinary to easily host the memes we generate.

So let’s get started.

#java #tutorial #heroku #memes #javascipt

Building a Meme Generator With RedwoodJS — The JavaScript Answer to Rails
Brad  Braun

Brad Braun

1613996700

20 Funny JavaScript Memes

I was scrolling through Reddit and remembered Atit’s post with 30 programming memes that made my day, so I decided to make one myself full of JavaScript memes for you guys. Enjoy!

1. JS devs be like:

Image for post

2. Aaaand that’s why you should probably use TypeScript

Image for post

3. Hehehe, what experience?

Image for post

#funny #javascript #coding #memes

20 Funny JavaScript Memes

The Pop Culture guide to authentication with Django REST framework, React and JWT

Tired of all the guides needing 500 frameworks and dependencies to get something reasonably simple to work? Then let’s throw all that out and use only what we need(but with some dumb pop culture references thrown in)! In this guide I’ll take you through the bare minimum of what you need to get started setting up a simple private route with user login,registration and some added in cool h’API views!

For a working repository if you are stuck, please look here

Requirements

  • Python 3.7 +
  • React 16.12
  • Django 3.1.2
  • Preferably VScode as this tutorial will make use of some of the quality of life features such as the terminal
  • Pip(will make your Python install etc much easier)
  • a tolerance for really bad memes

Abbreviations you will find

  • JSON web token (JWT)
  • Django REST framework (DRF)

1.Getting this ship off the ground

let’s start by creating the Django project, create a project folder open it in your code editor and open your terminal in there. Insert the below snippet and off we go!

let’s install our dependencies here and our pip environment

pipenv install

pipenv shell
pipenv install django djangorestframework djangorestframework-jwt django-cors-headers
please ensure you add the . at the end of the next part!
django-admin startproject <project-name> .

#jwt-auth #react #python #django-rest-framework #memes

The Pop Culture guide to authentication with Django REST framework, React and JWT

How Facial Expression Surrogates Augment Our Communication Online

Or: Why We’re Collectively Obsessed With Facebook’s Avatars, GIFs & Animal Crossing’s Villagers. In a meta-analysis of 30 trend reports for 2020, the most commonly predicted cultural shift was the continued blur between the physical and digital. Online and offline are progressively overlapping — from virtual clothes and AR makeup, to industrial digital twins and synthetic influencers. There’s only ever been one “real life”, but the line between the virtual and visceral is thinning. Expression surrogates augment our communication.

In light of the pandemic, with life moving online, this lack of distinction has only accelerated. We can Zoom to celebrate your birthday virtually, but the congregation still very much real. We’re together, but not together together. It’s all quite perplexing when we stop to think about it.

More noteworthy, the surge online has concentrated this top 2020 trend to one area: communication. Required to express thoughts and feelings without touch, direct eye contact, or the ability to discern micro expressions through screens, many are struggling, facing Zoom Fatigue. More, burdened with heavy, complex and unfamiliar emotions, the challenge of articulation compounds. It’s just too hard to say what we want or share what we mean, let alone deduce it all through the 0’s and 1’s.

One burgeoning solution? Delegate someone else to communicate for you.

#memes #communication #social-media-strategy #hackernoon-top-story #are-emoji-a-language #digital-identity #emoji #latest-tech-stories

How Facial Expression Surrogates Augment Our Communication Online
Aileen  Jacobs

Aileen Jacobs

1598426880

Are Machine Learning Memes Lying to You? : A Mathematica Investigation

I have taken severe umbrage at a particular machine learning meme that keeps popping up in my LinkedIn feed, so I decided to investigate if what it suggests is actually true.

Chihuahuas and Muffins

The widely circulated internet meme referencing machine learning is shown below and is an allegedly humorous parody on the confusion machine learning algorithms may have between in distinguishing between cute looking chihuahuas and delectable currantly infused muffins.

To the casual (human) observer, at first glance, it is possible to confuse the tempting baked comestible with the petite cutesy canine.

#mathematica #dogs #memes #machine-learning #muffins

Are Machine Learning Memes Lying to You? : A Mathematica Investigation
Noah  Rowe

Noah Rowe

1598314080

Meme Vision: the science of classifying memes

As a person of culture and science, I decided to build a model to identify memes. This problem is far simpler than the Image-Net competition and so a simpler solution is appropriate. I will demonstrate this by comparing the “Meme Vision” framework to ResNet-50 (the winner of Image-Net 2015).

Method: Meme Vision framework

In a previous article I explained the radial histogram method;

Radial Color Histograms

When color, composition and compute all matter for your computer vision problem — radially reduce the representation…

towardsdatascience.com

(TL;DR — it measures the distribution of color in each segment of the image)

Below we see how this can reduce images to very low dimensional representations.

Image for post

Basic radial color histogram example with 3 bins per color channel and 4 segments (giving 4*3³=108 features)

The final Meme Vision model uses a few extra steps:

  • Convert from RGB to HSV - color degradation is less of a problem to computers when viewed in the HSV palette.
  • Log transformation of pixel counts to help focus on the little differences.
  • Use 8 bins per channel (instead of 3) to distinguish similar color shades, which results 2048 features (instead of 108).
  • Feed these features into a linear support vector machine.

#image-recognition #optimisation #neural-networks #memes #image-classifier #neural networks

Meme Vision: the science of classifying memes

Lonero Needs the Hackernoon Community's Help

Hello World! It is about time, time that we asked you guys for help! I’m not talking about e-begging. What I’m talking about is that we want to stage a new site on StackOverFlow, but nobody knows about our proposal yet!

Visiting our StackOverFlow proposal hereand giving it a voluntary follow or engaging in discussions and questions let us know you are highly interested.

Well, you get free Lonero. I’m just kidding that is against StackOverFlow’s terms of service, and we aren’t charity. However, joining does let us know that you are interested and you are supporting people garnishing knowledge on technical questions they have on ever growing pieces of software being utilized to build a better, freer, more decentralized web.

#decentralized-web #decentralized-internet #blockchain #cryptocurrency #marketing #startups #bootstrap #memes

Lonero Needs the Hackernoon Community's Help
Java Questions

Java Questions

1597134360

The One Component That Deserves More Attention in Data Science

Introduction

Data science, machine learning, artificial intelligence, those terms are all over the news. They get everyone excited with the promises of automation, new savings or higher earnings, new features, markets or techniques. Some of those promises are well-founded, while others are still in inception or haven’t passed the proof-of-concept stage (another way to say they’re just at the wet-dreams stage).

There have been major improvements in techniques we use to extract, transform and load data. New and refined algorithms or techniques such as PCA, hyperparameter optimization, and designs, such as Neural Network, have brought improvements in outcomes. But there’s that one aspect that doesn’t get enough attention, the villain little duck. If you’re accustomed to working with data you might have already guessed it. If not, you’ll find out next. Let’s dive in.

The Unloved One In Data Science

At the heart of everything in business and research, aside from money, is data. Data is the new oil, or the new electricity depends on who you ask. A key asset. Computers make it easy to collect, share and analyse, it’s now a strategic asset.

But there’s an aspect about that that isn’t sufficiently discussed, it’s its quality. Quantity, whether Big Data or small data, doesn’t matter if the quality of the data is poor.

Garbage in, garbage out

No matter how good is your data pipeline, your cleaning and training/testing models, no matter your hypothesis, or the complexity of your algorithm, nothing valuable will result from your work if your data isn’t good or is of poor quality. That’s the famous “garbage in, garbage out”. You can’t bake a good cake with ripe ingredients.

This flow provides another way to look at data quality:

Data Quality → Information Quality → Decision Quality → Business Outcome

#data-science #data #data-governance #memes #business

The One Component That Deserves More Attention in Data Science
Vernie  Heller

Vernie Heller

1595622240

Creating memes in excel, using python

Yep, you saw it right! It’s an actual meme drawn on excel. Each of the cells in the excel sheet are color-formatted as per the original image, recreating the famous ‘surprised pikachu’ meme.

So how do we draw the meme (or for that matter, any image) on excel? Needless to say, manually editing each cell is an impossible task. Therefore, we will write a python script that completes the job for us. This script does four simple tasks:

  1. Read the original image and resize it, if required. Note down the RGB values for each pixel in the image.
  2. Create a blank excel file, and appropriately resize the cells in the sheet. Resizing is important as pixels in image are square, while excel cells are rectangular by default.
  3. Make any optional adjustments, such as providing margin and background color, conversion to black and white or setting zoom level for excel sheet.
  4. For each pixel, fill a corresponding cell with the RGB values.

We use two python libraries: PIL for image processing and openpyxl for excel manipulation. Both these libraries are pre-installed in anaconda package.

We start by importing the necessary libraries and reading (and resizing) the original image:

from PIL import Image
import openpyxl

im = Image.open('meme.jpg', 'r')
width = max_width
height = int(width*(im.size[1]/im.size[0]))
im = im.resize((width, height))
px = im.load()

‘max_width’ is an adjustable parameter that I’ve set according to my laptop’s screen size. The code has several such parameters whose values are declared at the start of the code:

max_width = 350
sheet_zoom = 15        # Zeem level of the excel sheet
column_size = 2
row_size = 10
img_left_margin = 10
img_top_margin = 10
img_right_margin = 10
img_bottom_margin = 10
bg_col = 'FFFFFF'      # Background colour - default is white
black_and_white = 0    # Set to 1 if you want black and white image

Once we’ve read the image, we create a blank excel file and resize the cells:

wb = openpyxl.Workbook()
ws = wb.active

for i in range(0, height+img_top_margin+img_bottom_margin):
    t = ws.cell(row = i+1, column = 1)
    ws.row_dimensions[t.row].height = row_size
for j in range(0, width+img_left_margin+img_right_margin):
    t = ws.cell(row = 1, column = j+1)
    ws.column_dimensions[t.coordinate.replace(str(t.row),'')].width = column_size

We can also change the background color:

for i in range(0, width+img_left_margin+img_right_margin):
    for j in range(0, height+img_top_margin+img_bottom_margin):
        a = ws.cell(row = j+1, column = i+1)
        a.fill = openpyxl.styles.PatternFill(fill_type = 'solid', start_color = bg_col, end_color = bg_col)

Finally, we take an array of cells equal in size to the image resolution, and color each cell in the array with the corresponding pixel’s RGB values.

for i in range(0, width):
    for j in range(0, height):
        a = ws.cell(row = j+img_top_margin, column = i+img_left_margin)
        col = px[i, j]
        c1 = col[0]
        c2 = col[1]
        c3 = col[2]

        if black_and_white == 1:
            bw = int(0.2126*c1 + 0.7152*c2 + 0.0722*c3)
            val = '{:02X}'.format(bw)
            c = val + val + val
        else:
            c = '{:02X}{:02X}{:02X}'.format(c1, c2, c3)
        c_fill = openpyxl.styles.PatternFill(fill_type = 'solid', start_color = c, end_color = c)
        a.fill = c_fill
ws.sheet_view.zoomScale = sheet_zoom
wb.save(filename = 'meme_pic.xlsx')

#image #python #memes

Creating memes in excel, using python