Gracelyn Wills

Gracelyn Wills


Why I have to create Tron tokens?

Tron blockchain has been an emerging blockchain since 2018. Ever since bitcoin is popularizing, crypto users and crypto entrepreneurs have been increasing. Meanwhile, the technology itself is evolving to a variable phase.

We all know that ethereum is the top crypto token development platform for many startups and entrepreneurs. Once Tron was introduced as an erc20 token, it has become a large blockchain itself now. One of the demanded services among the Crypto space is to create TRC20 tokens.

So what is in it for entrepreneurs to create a crypto token in the first place?

You see, there are money generating ways in the crypto space. One of the most preferred, reliable and easiest ways to get profits is by creating crypto tokens. You can launch an ICO to raise funds and issue created crypto tokens for the buyers of your services. Also, you can use created crypto tokens for Airdrops for marketing strategies. Despite fundraising and market making, crypto tokens are used for many business purposes.

How to create Tron tokens?

If you a tech-savvy person, you can create Tron tokens in no time. Anyway, you have to pay a minimal TRX for creating and mining tokens for your wallets. Also, the technical difficulties are quite complex to understand and execute. Not to mention the smart contracts, smart contracts are immutable. Once implemented into the Blockchain there are no ways to alter or change it.

You got to discuss your business requirements with an expert team of Blockchain experts. I would recommend you hire a Blockchain experts team from Zab technologies. Being a leading Tron token development company, they offer best-in-class featured services.

Talk to their Blockchain experts via,

Whatsapp: +91 77085 29089
skype: live:contact_86571

#tron #blockchain #tokenization #startups #entrepreneurs

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Buddha Community

Why I have to create Tron tokens?
Easter  Deckow

Easter Deckow


PyTumblr: A Python Tumblr API v2 Client



Install via pip:

$ pip install pytumblr

Install from source:

$ git clone
$ cd pytumblr
$ python install


Create a client

A pytumblr.TumblrRestClient is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:

client = pytumblr.TumblrRestClient(
) # Grabs the current user information

Two easy ways to get your credentials to are:

  1. The built-in tool (if you already have a consumer key & secret)
  2. The Tumblr API console at
  3. Get sample login code at

Supported Methods

User Methods # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user

client.follow('') # follow a blog
client.unfollow('') # unfollow a blog, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a post

Blog Methods

client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blog

Post Methods

Creating posts

PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.

The default supported types are described below.

  • state - a string, the state of the post. Supported types are published, draft, queue, private
  • tags - a list, a list of strings that you want tagged on the post. eg: ["testing", "magic", "1"]
  • tweet - a string, the string of the customized tweet you want. eg: "Man I love my mega awesome post!"
  • date - a string, the customized GMT that you want
  • format - a string, the format that your post is in. Support types are html or markdown
  • slug - a string, the slug for the url of the post you want

We'll show examples throughout of these default examples while showcasing all the specific post types.

Creating a photo post

Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload

#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],

#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
                    tweet="Woah this is an incredible sweet post [URL]",

#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
                    data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
                    caption="## Mega sweet kittens")

Creating a text post

Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html

#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")

Creating a quote post

Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported

#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")

Creating a link post

  • title - a string, the title of post that you want. Supports HTML entities.
  • url - a string, the url that you want to create a link post for.
  • description - a string, the desciption of the link that you have
#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="",
                   description="Search is pretty cool when a duck does it.")

Creating a chat post

Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)

#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])

Creating an audio post

Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr

#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")

#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="")

Creating a video post

Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload

#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",

#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/")

Editing a post

Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.

client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")

Reblogging a Post

Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.

client.reblog(blogName, id=125356, reblog_key="reblog_key")

Deleting a post

Deleting just requires that you own the post and have the post id

client.delete_post(blogName, 123456) # Deletes your post :(

A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):

client.create_text(blogName, tags=['hello', 'world'], ...)

Getting notes for a post

In order to get the notes for a post, you need to have the post id and the blog that it is on.

data = client.notes(blogName, id='123456')

The results include a timestamp you can use to make future calls.

data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])

Tagged Methods

# get posts with a given tag
client.tagged(tag, **params)

Using the interactive console

This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).

You'll need pyyaml installed to run it, but then it's just:

$ python

and away you go! Tokens are stored in ~/.tumblr and are also shared by other Tumblr API clients like the Ruby client.

Running tests

The tests (and coverage reports) are run with nose, like this:

python test

Author: tumblr
Source Code:
License: Apache-2.0 license

#python #api 

aaron silva

aaron silva


SafeMoon Clone | Create A DeFi Token Like SafeMoon | DeFi token like SafeMoon

SafeMoon is a decentralized finance (DeFi) token. This token consists of RFI tokenomics and auto-liquidity generating protocol. A DeFi token like SafeMoon has reached the mainstream standards under the Binance Smart Chain. Its success and popularity have been immense, thus, making the majority of the business firms adopt this style of cryptocurrency as an alternative.

A DeFi token like SafeMoon is almost similar to the other crypto-token, but the only difference being that it charges a 10% transaction fee from the users who sell their tokens, in which 5% of the fee is distributed to the remaining SafeMoon owners. This feature rewards the owners for holding onto their tokens.

Read More @

#create a defi token like safemoon #defi token like safemoon #safemoon token #safemoon token clone #defi token

Shubham Ankit

Shubham Ankit


How to Automate Excel with Python | Python Excel Tutorial (OpenPyXL)

How to Automate Excel with Python

In this article, We will show how we can use python to automate Excel . A useful Python library is Openpyxl which we will learn to do Excel Automation


Openpyxl is a Python library that is used to read from an Excel file or write to an Excel file. Data scientists use Openpyxl for data analysis, data copying, data mining, drawing charts, styling sheets, adding formulas, and more.

Workbook: A spreadsheet is represented as a workbook in openpyxl. A workbook consists of one or more sheets.

Sheet: A sheet is a single page composed of cells for organizing data.

Cell: The intersection of a row and a column is called a cell. Usually represented by A1, B5, etc.

Row: A row is a horizontal line represented by a number (1,2, etc.).

Column: A column is a vertical line represented by a capital letter (A, B, etc.).

Openpyxl can be installed using the pip command and it is recommended to install it in a virtual environment.

pip install openpyxl


We start by creating a new spreadsheet, which is called a workbook in Openpyxl. We import the workbook module from Openpyxl and use the function Workbook() which creates a new workbook.

from openpyxl
import Workbook
#creates a new workbook
wb = Workbook()
#Gets the first active worksheet
ws =
#creating new worksheets by using the create_sheet method

ws1 = wb.create_sheet("sheet1", 0) #inserts at first position
ws2 = wb.create_sheet("sheet2") #inserts at last position
ws3 = wb.create_sheet("sheet3", -1) #inserts at penultimate position

#Renaming the sheet
ws.title = "Example"

#save the workbook = "example.xlsx")


We load the file using the function load_Workbook() which takes the filename as an argument. The file must be saved in the same working directory.

#loading a workbook
wb = openpyxl.load_workbook("example.xlsx")




#getting sheet names
result = ['sheet1', 'Sheet', 'sheet3', 'sheet2']

#getting a particular sheet
sheet1 = wb["sheet2"]

#getting sheet title
result = 'sheet2'

#Getting the active sheet
sheetactive =
result = 'sheet1'




#get a cell from the sheet
sheet1["A1"] <
  Cell 'Sheet1'.A1 >

  #get the cell value
ws["A1"].value 'Segment'

#accessing cell using row and column and assigning a value
d = ws.cell(row = 4, column = 2, value = 10)




#looping through each row and column
for x in range(1, 5):
  for y in range(1, 5):
  print(x, y, ws.cell(row = x, column = y)

#getting the highest row number

#getting the highest column number

There are two functions for iterating through rows and columns.

Iter_rows() => returns the rows
Iter_cols() => returns the columns {
  min_row = 4, max_row = 5, min_col = 2, max_col = 5
} => This can be used to set the boundaries
for any iteration.


#iterating rows
for row in ws.iter_rows(min_row = 2, max_col = 3, max_row = 3):
  for cell in row:
  print(cell) <
  Cell 'Sheet1'.A2 >
  Cell 'Sheet1'.B2 >
  Cell 'Sheet1'.C2 >
  Cell 'Sheet1'.A3 >
  Cell 'Sheet1'.B3 >
  Cell 'Sheet1'.C3 >

  #iterating columns
for col in ws.iter_cols(min_row = 2, max_col = 3, max_row = 3):
  for cell in col:
  print(cell) <
  Cell 'Sheet1'.A2 >
  Cell 'Sheet1'.A3 >
  Cell 'Sheet1'.B2 >
  Cell 'Sheet1'.B3 >
  Cell 'Sheet1'.C2 >
  Cell 'Sheet1'.C3 >

To get all the rows of the worksheet we use the method worksheet.rows and to get all the columns of the worksheet we use the method worksheet.columns. Similarly, to iterate only through the values we use the method worksheet.values.


for row in ws.values:
  for value in row:



Writing to a workbook can be done in many ways such as adding a formula, adding charts, images, updating cell values, inserting rows and columns, etc… We will discuss each of these with an example.




#creates a new workbook
wb = openpyxl.Workbook()

#saving the workbook"new.xlsx")




#creating a new sheet
ws1 = wb.create_sheet(title = "sheet 2")

#creating a new sheet at index 0
ws2 = wb.create_sheet(index = 0, title = "sheet 0")

#checking the sheet names
wb.sheetnames['sheet 0', 'Sheet', 'sheet 2']

#deleting a sheet
del wb['sheet 0']

#checking sheetnames
wb.sheetnames['Sheet', 'sheet 2']




#checking the sheet value

#adding value to cell
ws['B2'] = 367

#checking value




We often require formulas to be included in our Excel datasheet. We can easily add formulas using the Openpyxl module just like you add values to a cell.

For example:

import openpyxl
from openpyxl
import Workbook

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']

ws['A9'] = '=SUM(A2:A8)'"new2.xlsx")

The above program will add the formula (=SUM(A2:A8)) in cell A9. The result will be as below.




Two or more cells can be merged to a rectangular area using the method merge_cells(), and similarly, they can be unmerged using the method unmerge_cells().

For example:
Merge cells

#merge cells B2 to C9
ws['B2'] = "Merged cells"

Adding the above code to the previous example will merge cells as below.




#unmerge cells B2 to C9

The above code will unmerge cells from B2 to C9.


To insert an image we import the image function from the module openpyxl.drawing.image. We then load our image and add it to the cell as shown in the below example.


import openpyxl
from openpyxl
import Workbook
from openpyxl.drawing.image
import Image

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
#loading the image(should be in same folder)
img = Image('logo.png')
ws['A1'] = "Adding image"
#adjusting size
img.height = 130
img.width = 200
#adding img to cell A3

ws.add_image(img, 'A3')"new2.xlsx")




Charts are essential to show a visualization of data. We can create charts from Excel data using the Openpyxl module chart. Different forms of charts such as line charts, bar charts, 3D line charts, etc., can be created. We need to create a reference that contains the data to be used for the chart, which is nothing but a selection of cells (rows and columns). I am using sample data to create a 3D bar chart in the below example:


import openpyxl
from openpyxl
import Workbook
from openpyxl.chart
import BarChart3D, Reference, series

wb = openpyxl.load_workbook("example.xlsx")
ws =

values = Reference(ws, min_col = 3, min_row = 2, max_col = 3, max_row = 40)
chart = BarChart3D()
ws.add_chart(chart, "E3")"MyChart.xlsx")


How to Automate Excel with Python with Video Tutorial

Welcome to another video! In this video, We will cover how we can use python to automate Excel. I'll be going over everything from creating workbooks to accessing individual cells and stylizing cells. There is a ton of things that you can do with Excel but I'll just be covering the core/base things in OpenPyXl.

⭐️ Timestamps ⭐️
00:00 | Introduction
02:14 | Installing openpyxl
03:19 | Testing Installation
04:25 | Loading an Existing Workbook
06:46 | Accessing Worksheets
07:37 | Accessing Cell Values
08:58 | Saving Workbooks
09:52 | Creating, Listing and Changing Sheets
11:50 | Creating a New Workbook
12:39 | Adding/Appending Rows
14:26 | Accessing Multiple Cells
20:46 | Merging Cells
22:27 | Inserting and Deleting Rows
23:35 | Inserting and Deleting Columns
24:48 | Copying and Moving Cells
26:06 | Practical Example, Formulas & Cell Styling

📄 Resources 📄
OpenPyXL Docs: 
Code Written in This Tutorial: 


Rowan Benny


Developing Chatbots project

If you want your business to prosper, you'll have to stay on top of the latest trends. The creation of a chatbot is a lengthy procedure. However, if well planned, it can be a piece of cake. The emergence of chatbots is one of the most significant recent developments in the area of customer care. On that topic, chatbots are one of the most well-known marketing tools in use today, aiding in the development of effective communication between businesses and their customers. So, read on to learn about data science projects for final year students as well as data science projects for beginners.

When it comes to chatbot creation, the most important thing to remember is to break the process down into simple steps and follow them one by one. Chatbots are quite handy if you want to improve your customer's experience by answering their questions, reducing human workload, performing remote troubleshooting, and so on. Rather than adopting a bot development framework or another platform, why not build a basic, intelligent chatbot from the ground up using deep learning? Though bots have a wide range of applications, one of the most well-known is live chat platforms, where users ask queries and a chatbot responds appropriately. There are different types of recommendation systems of the data science projects ideas.

So, in order to make your life easier, we've provided step-by-step chatbot programming guidelines. The days of waiting (not so patiently) on hold for answers to your most pressing questions are quickly fading away. In this lesson, you'll learn how to use Keras to create an end-to-end domain-specific intelligent chatbot solution.


A chatbot is a piece of software that can communicate and conduct tasks in the same way that a human can. Because we're going to build a deep learning model, we'll need data to train it. Chatbots are marketing and automation solutions that are supposed to assist people by interacting with them and performing human-like interactions. Chatbots are widely utilised in customer service, social media marketing, and client instant messaging.

However, because this is a rudimentary chatbot, we will neither collect nor download any significant datasets. To communicate, these bots may employ Natural Language Processing (NLP) or audio analysis techniques, making them sound more natural. Based on how they're developed, there are two primary sorts of chatbot models: retrieval-based and generation-based models. These intentions may differ from one

chatbot solution to the next depending on the domain in which you are implementing a chatbot solution. AI-Chatbots are widely recommended by entrepreneurs and organizations. Let's take this data science project step by step.

Import and load the data file

import nltk

from nltk.stem import WordNetLemmatizer

lemmatizer = WordNetLemmatizer()

import json

import pickle

import numpy as np

from keras.models import Sequential

from keras.layers import Dense, Activation, Dropout

from keras.optimizers import SGD

import random


classes = []

documents = []

ignore_words = ['?', '!']

data_file = open('intents.json').read()

intents = json.loads(data_file)

Preprocess data 

for intent in intents['intents']:

    for pattern in intent['patterns']:

     #tokenize each word

     w = nltk.word_tokenize(pattern)


     #add documents in the corpus

     documents.append((w, intent['tag']))

           if intent['tag'] not in classes:


Create training and testing data 

training = []


output_empty = [0] * len(classes)


for doc in documents:


    bag = []

Build the model

model = Sequential()

model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))


model.add(Dense(64, activation='relu'))


model.add(Dense(len(train_y[0]), activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

hist =, np.array(train_y), epochs=200, batch_size=5, verbose=1)'chatbot_model.h5', hist)

print("model created"

 output_row = list(output_empty)

    output_row[classes.index(doc[1])] = 1

    training.append([bag, output_row])


training = np.array(training)

train_x = list(training[:,0])

train_y = list(training[:,1])

print("Training data created")

Predict the response (Graphical User Interface)

import nltk

from nltk.stem import WordNetLemmatizer

lemmatizer = WordNetLemmatizer()

import pickle

import numpy as np

from keras.models import load_model

model = load_model('chatbot_model.h5')

import json

import random

intents = json.loads(open('intents.json').read())

words = pickle.load(open('words.pkl','rb'))

def clean_up_sentence(sentence):

sentence_words = nltk.word_tokenize(sentence)

 return sentence_words

def bow(sentence, words, show_details=True):

 sentence_words = clean_up_sentence(sentence)

bag = [0]*len(words)

    for s in sentence_words:

     for i,w in enumerate(words):

         if w == s:

             bag[i] = 1

             if show_details:

                 print ("found in bag: %s" % w)


def predict_class(sentence, model):

 p = bow(sentence, words,show_details=False)

    res = model.predict(np.array([p]))[0]


     results.sort(key=lambda x: x[1], reverse=True)

    return_list = []

    for r in results:

        return_list.append({"intent": classes[r[0]], "probability": str(r[1])})

    return return_list

.def getResponse(ints, intents_json):

    tag = ints[0]['intent']

    list_of_intents = intents_json['intents']

    for i in list_of_intents:

     if(i['tag']== tag):

         result = random.choice(i['responses'])


    return result

def chatbot_response(text):

    ints = predict_class(text, model)

    res = getResponse(ints, intents)

    return res

#Creating GUI with tkinter

import tkinter

from tkinter import *

def send():

    msg = EntryBox.get("1.0",'end-1c').strip()


    if msg != '':


     ChatLog.insert(END, "You: " + msg + '\n\n')

     ChatLog.config(foreground="#442265", font=("Verdana", 12 ))

     res = chatbot_response(msg)

     ChatLog.insert(END, "Bot: " + res + '\n\n')



base = Tk()



base.resizable(width=FALSE, height=FALSE)

#Create Chat window


#Bind scrollbar to Chat window

scrollbar = Scrollbar(base, command=ChatLog.yview, cursor="heart")

ChatLog['yscrollcommand'] = scrollbar.set

#Create Button to send message

SendButton = Button(base, font=("Verdana",12,'bold'), text="Send", width="12", height=5,

                 bd=0, bg="#32de97", activebackground="#3c9d9b",fg='#ffffff',

                 command= send )

#Create the box to enter message

EntryBox = Text(base, bd=0, bg="white",width="29", height="5", font="Arial")

#EntryBox.bind("", send)

#Place all components on the screen,y=6, height=386),y=6, height=386, width=370), y=401, height=90, width=265), y=401, height=90)


If you want to learn more about how to do data science projects step by step, visit our website Learnbay: data science course in Chennai.



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Tron Token


Tron - What is TRX? Is it Worth Buying Tron in 2021?

Tron is one of the most popular cryptocurrencies in this market, practically since its birth in 2017,
Opinions are strongly divided between those who, on the one hand, believe in the value of the project and, on the other, believe that the currency is no more than a fad.
In part, this is also due to the personality of its CEO, Justin Sun, who does everything to be media, since he knows that this is a way to boost the growth of the project.
Tron has competed to be among the top cryptocurrencies, conquering a place in the Top 10 of the market repeatedly. But why? Could it be that this project has value? In this article, we will make it clear what the objective of this cryptocurrency is and how you can invest in Tron safely.

What is Tron?
The project of this cryptocurrency was born in 2017, created by Justin Sun, current CEO of Tron Foundation - a non-profit company based in Singapore.
Its token is known as Tronix (TRX). It initially started as an ERC20 token (that is, it existed on the Ethereum blockchain ).
However, in 2018 Tron successfully concluded the launch of its blockchain.
Tron aims to build a decentralized ecosystem of entertainment content, which facilitates the creation and dissemination of the same.

One Step Back …
To better contextualize, it is necessary to take a step back and understand the business model of the entertainment platforms that lead the current market …
Platforms such as YouTube host the content produced by our favorite creators ( streamers, YouTubers, series, songs, etc.) which we can later access and consume for hours and hours of entertainment.
What do those platforms gain from that?
Access to this content may be free or require a monthly subscription or purchase of the service.
Not forgetting that being free, we are always subject to advertisements that can often be considered invasive.
Part of the profit generated is shared with the creators of the content, under the terms determined by the platforms that host the content.
However, these platforms have great power of control over the content and its respective creators.
They control all the variables that determine the possible views and profits generated by these contents using a centralized business model.
And that can be a problem.
The review of content creators has been increasingly frequent, demonstrating their dissatisfaction with the policies of censorship that demonetizes the content that is placed on these platforms.
The same applies to app stores, a market currently dominated by Apple and Google, which make mobile operating systems on virtually all cell phones and tablets.

Two Steps Ahead?
Drawing on the innovative features of blockchain, [TRON token development] aims to approximate the relationship between consumers and content creators.
This proximity is created by eliminating intermediate elements of this process.
The intermediaries in question are the entertainment platforms that control and receive a “fat” share of the profits that belong to the content creators.
In summary:
The current business model flaw that TRON intends to explore is the centralization of the entertainment traffic that exists on the Internet.
These contents are mostly controlled by a small number of large companies such as Google, Facebook, Amazon, and the like, which in turn control platforms such as YouTube, Twitch, Google Play, etc

How does Tron work?
Tron architecture.
This illustration represents the current architecture of this project, which is divided into 3 stages:
Applications - where developers can create and install decentralized applications, as well as create and customize their tokens on the Tron blockchain;
Core - where the main components of the protocol are, such as smart contracts, software development kits (SDK), and other modules that are used in the creation of decentralized applications;
Storage - Tron applies a distributed data storage model on its blockchain, which allows for rapid processing and updating.
To achieve these goals, the TRON protocol aims to take advantage of peer-to-peer (P2P) technology and the blockchain.
The TRON blockchain uses the consensus algorithm known as Delegated Proof-of-Stake - an algorithm derived from Proof of Stake that allows TRX holders to generate passive income.
This means that when buying TRX you can store them in a wallet that allows you to stake your coins.
In this way, you are often rewarded with more TRX for your contribution to the Tron network.
The Tron blockchain supposedly can process 2,000 transactions per second (TPS). A number was much higher than the 7 TPS of the Bitcoin network. However, this information has not yet been verified by third parties.

Some Associations
TRX can have other uses thanks to some associations that have emerged, some of which are: - Tron created a partnership with this online gaming platform to extend its presence in the gaming industry.
Gift - A decentralized platform where it is possible to offer virtual gifts.
Peiwo - A social media network, also founded by Justin Sun, often referred to as China’s Snapchat.
Alliance announcements and rumors have always contributed to the controversy surrounding this project, leading many crypto enthusiasts to label TRON a " shitcoin " due to these aggressive marketing tactics.
However, it is safe to say that [Tron token development]is a well-positioned cryptocurrency for exploring the Asian social media and entertainment market. The volume of traffic is huge!

It is on the Tron blockchain that one of the most popular dapps resides: Wink (previously TronBet).
Wink is a decentralized gambling and casino gaming platform that allows you to use TRX for gambling.
Developers can create games and then be rewarded depending on the success of the game.
This dApp is one of the largest, not only on the TRON blockchain but in the entire market.
It has an average of 10,000 users per week, handling 1,351 trillion TRX

The Personification Of The Project
TRON CEO Justin Sun is a well-known “crypto-celebrity.” At the age of 30, he has in his CV positions such as Chief Representative of Ripple, CEO of BitTorrent, and now CEO of Tron.
Justin is one of the great reasons why this cryptocurrency has a profile surrounded by so much controversy.
His personality and behavior on social media are notorious, particularly for his marketing tactics.
Some see him as a role model, while others call him an “imposter.”
Despite what people may think, the truth is that TRON is one of the most popular cryptocurrencies in the media, something that always influences the price of a coin.

Acquisition of BitTorrent
In 2019, Tron bought BitTorrent (BTT), a cryptocurrency created on the Tron blockchain, and with a vision aligned to decentralize the Internet.
How to Buy Tron (TRX)?
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When investing through a broker, the need to configure virtual wallets (discarded wallets ) to keep your safe criptomonedas , which is a Recommended Practice for the time to buy in a bag.
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In addition to other advantages, this broker offers tools called CFDs.
With CFDs, you can make a profit with the rise and fall of the price of cryptocurrencies, unlike other options. This is one of the few types of investment in which that happens.

TRX price
On the date of its ICO, in September 2017, a TRX cost approximately 0.002 USD. That is, less than 1 cent per TRX!

The price of Tron reached an all-time high of $ 0.254 in January 2018, which represented, at height, a growth of 125 times its initial value.

Since then, it had a great correction in its price that, to tell the truth, affected the entire cryptocurrency market at the beginning of 2018.
2020 was a good year for Tron. This cryptocurrency grew close to 80%, once again encouraging its investors. However, this value is well below what it achieved in other years.

TRON’s goals are undoubtedly ambitious!
Decentralizing the business model of big tech companies and bringing power back to content creators would undoubtedly be a game-changing feat.
With everything you’ve shown so far, it can be said with some confidence that this is not just a shitcoin (dubious project).
It would be good to see the final result of some of their collaborations already obtained to understand more clearly all that Tron can achieve.
We recognize that, like many others in this market, this project is in an early stage, despite the large network of business contacts that it has already achieved.

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