1673072651
In this article, we'll discuss information about the ShibaInu Finance project and SHIF token. What is ShibaInu Finance (SHIF) | What is SHIF token?
Shibainu Finance is the leading decentralized exchange on BNB Smart Chain, with the highest trading volumes in the market
Shibainu Finance runs on BNB Smart Chain, a blockchain with much lower transaction costs than Ethereum or Bitcoin. Trading fees are lower than other top decentralized exchanges too, so that's a double win for you!
Trade directly from your wallet app. Unlike centralized exchanges like Binance or Coinbase, Shibainu Finance doesn’t hold your funds when you trade: you have 100% ownership of your own crypto.
Earn SHIF and other tokens for free with super high interest rates.
Stake SHIF, earn free tokens. It’s really that easy. SHIF holders right now are earning tens of millions of USD worth of free tokens each week from major projects. New projects join the party frequently, so you can earn more, for even longer.
Stake LP tokens, earn SHIF. You take on a little more exposure to market fluctuations than with the Staking Pools, but can earn higher APR to offset the risk.
1. Exchange
Shibainu Finance is an Automated Market Maker (AMM), and the Exchange is at the heart of Shibainu Finance. Shibainu Finance is the leading AMM on the BNB Smart Chain, and as statistics tell, the most popular Decentralized Exchange (DEX) ever!
The Shibainu Finance Exchange offers several features that support decentralized trading:
Swapping/Trading:
Liquidity Pools:
Yield Farming
Private-sale
2. Staking Pools - Stake SHIF
Staking Pools are the simplest way to earn free tokens on Shibainu Finance. Stake SHIF and earn free tokens. It’s really that easy.
Some special pools let you stake other tokens besides SHIF, too!
How can I use Staking Pools?
3. StableSwap
StableSwap on Shibainu Finance is a feature to trade stable pairs with a lower slippage based on an invariant curve slippage function. It is designed to swap specific assets that are priced closely – such as USD stablecoins (e.g. HAY, BUSD and USDT) or liquid staking tokens (e.g. stkBNB and aBNBc).
The StableSwap is an implementation of Curve Finance’s AMM on Shibainu Finance. It adds linear invariant constant sum curve (x+y=k) on top of the constant product formula (x*y=k) to keep prices more equal as long as the liquidity pool is not extremely unbalanced. As a result, since StableSwaps are restricted to similarly priced assets, impermanent loss is not as much of a concern (except in extreme depeg cases) and the slippage is lower than normal AMM which just uses the constant product formula.
When you conduct a Swap (trade) on the StableSwap you will pay lower trading fees, than the usual 0.25% on normal Shibainu Finance AMM. The fee attribution is broken down as follows:
Stableswap Fees
Fees for pairs are broken down in the table below:
Stablepair | Trading Fees | LP Rewards | SHIF Buyback | Shibainu Finance Treasury |
---|---|---|---|---|
USDT-BUSD | 0.15% | 0.075% | 0.06% | 0.015% |
USDC-BUSD | 0.15% | 0.075% | 0.06% | 0.015% |
USDC-USDT | 0.15% | 0.075% | 0.06% | 0.015% |
HAY-BUSD | 0.04% | 0.02% | 0.016% | 0.004% |
Why should I use the StableSwap instead of the normal AMM Swap?
3. SHIF- Native Token
To facilitate a stable environment for the complex DeFi tools and functionalities, Shibainu Finance relies on its native token SHIF. SHIF is a BEP20 token on the Binance Smart Chain, which serves as the backbone for most staking and liquidity pools on the platform. It serves as a reward token and the main currency on the platform.
Key metrics
Distribution
Fund raising
SHIF token use cases
SHIF is the delicious token that powers the Shibainu Finance platform.
Earn SHIF from Farming and Staking, then explore its use cases:
4. Roadmap
Phase 1
Phase 2
Phase 3
Phase 4
How and Where to Buy SHIF token?
SHIF has been listed on a number of crypto exchanges, unlike other main cryptocurrencies, it cannot be directly purchased with fiats money. However, You can still easily buy this coin by first buying Bitcoin, ETH, USDT, BNB from any large exchanges and then transfer to the exchange that offers to trade this coin, in this guide article we will walk you through in detail the steps to buy SHIF token
You will have to first buy one of the major cryptocurrencies, usually either Bitcoin (BTC), Ethereum (ETH), Tether (USDT), Binance (BNB)…
We will use Binance Exchange here as it is one of the largest crypto exchanges that accept fiat deposits.
Once you finished the KYC process. You will be asked to add a payment method. Here you can either choose to provide a credit/debit card or use a bank transfer, and buy one of the major cryptocurrencies, usually either Bitcoin (BTC), Ethereum (ETH), Tether (USDT), Binance (BNB)…
Once finished you will then need to make a BTC/ETH/USDT/BNB deposit to the exchange from Binance depending on the available market pairs. After the deposit is confirmed you may then purchase SHIF from the exchange.
The top exchange for trading in SHIF token is currently: PancakeSwap (V2).
BEP-20 contracts: 0xA5d2D170c75520d510f1dAd6254BEE8475479032
Read more: What is Pancakeswap | Beginner's Guide on How to Use Pancakeswap
Find more information SHIF token ☞ Website
I hope this post will help you. Don't forget to leave a like, comment and sharing it with others. Thank you!
🔺DISCLAIMER: The Information in the post isn’t financial advice, is intended FOR GENERAL INFORMATION PURPOSES ONLY. Trading Cryptocurrency is VERY risky. Make sure you understand these risks and that you are responsible for what you do with your money.
#bitcoin #cryptocurrency #token #coin
1624219980
NFT Art Finance is currently one of the most popular cryptocurrencies right now on the market, so in today’s video, I will be showing you guys how to easily buy NFT Art Finance on your phone using the Trust Wallet application.
📺 The video in this post was made by More LimSanity
The origin of the article: https://www.youtube.com/watch?v=sKE6Pc_w1IE
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
Cryptocurrency trading is VERY risky. Make sure you understand these risks and that you are responsible for what you do with your money
🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner
⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!
☞ **-----CLICK HERE-----**⭐ ⭐ ⭐
Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!
#bitcoin #blockchain #nft art finance token #token #buy nft art finance #how to buy nft art finance token - the easiest method!
1624312800
SPORE FINANCE PREDICTION - WHAT IS SPORE FINANCE & SPORE FINANCE ANALYSIS - SPORE FINANCE
In this video, I talk about spore finance coin and give my spore finance prediction. I talk about the latest spore finance analysis & spore finance crypto coin that recently has been hit pretty hard in the last 24 hours. I go over what is spore finance and how many holders are on this new crypto coin spore finance.
📺 The video in this post was made by Josh’s Finance
The origin of the article: https://www.youtube.com/watch?v=qbPQvdxCtEI
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
Cryptocurrency trading is VERY risky. Make sure you understand these risks and that you are responsible for what you do with your money
🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner
⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!
☞ **-----CLICK HERE-----**⭐ ⭐ ⭐
Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!
#bitcoin #blockchain #spore finance #what is spore finance #spore finance prediction - what is spore finance & spore finance analysis - spore finance #spore finance prediction
1658068560
WordsCounted
We are all in the gutter, but some of us are looking at the stars.
-- Oscar Wilde
WordsCounted is a Ruby NLP (natural language processor). WordsCounted lets you implement powerful tokensation strategies with a very flexible tokeniser class.
["Bayrūt"]
and not ["Bayr", "ū", "t"]
, for example.Add this line to your application's Gemfile:
gem 'words_counted'
And then execute:
$ bundle
Or install it yourself as:
$ gem install words_counted
Pass in a string or a file path, and an optional filter and/or regexp.
counter = WordsCounted.count(
"We are all in the gutter, but some of us are looking at the stars."
)
# Using a file
counter = WordsCounted.from_file("path/or/url/to/my/file.txt")
.count
and .from_file
are convenience methods that take an input, tokenise it, and return an instance of WordsCounted::Counter
initialized with the tokens. The WordsCounted::Tokeniser
and WordsCounted::Counter
classes can be used alone, however.
WordsCounted.count(input, options = {})
Tokenises input and initializes a WordsCounted::Counter
object with the resulting tokens.
counter = WordsCounted.count("Hello Beirut!")
Accepts two options: exclude
and regexp
. See Excluding tokens from the analyser and Passing in a custom regexp respectively.
WordsCounted.from_file(path, options = {})
Reads and tokenises a file, and initializes a WordsCounted::Counter
object with the resulting tokens.
counter = WordsCounted.from_file("hello_beirut.txt")
Accepts the same options as .count
.
The tokeniser allows you to tokenise text in a variety of ways. You can pass in your own rules for tokenisation, and apply a powerful filter with any combination of rules as long as they can boil down into a lambda.
Out of the box the tokeniser includes only alpha chars. Hyphenated tokens and tokens with apostrophes are considered a single token.
#tokenise([pattern: TOKEN_REGEXP, exclude: nil])
tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise
# With `exclude`
tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise(exclude: "hello")
# With `pattern`
tokeniser = WordsCounted::Tokeniser.new("I <3 Beirut!").tokenise(pattern: /[a-z]/i)
See Excluding tokens from the analyser and Passing in a custom regexp for more information.
The WordsCounted::Counter
class allows you to collect various statistics from an array of tokens.
#token_count
Returns the token count of a given string.
counter.token_count #=> 15
#token_frequency
Returns a sorted (unstable) two-dimensional array where each element is a token and its frequency. The array is sorted by frequency in descending order.
counter.token_frequency
[
["the", 2],
["are", 2],
["we", 1],
# ...
["all", 1]
]
#most_frequent_tokens
Returns a hash where each key-value pair is a token and its frequency.
counter.most_frequent_tokens
{ "are" => 2, "the" => 2 }
#token_lengths
Returns a sorted (unstable) two-dimentional array where each element contains a token and its length. The array is sorted by length in descending order.
counter.token_lengths
[
["looking", 7],
["gutter", 6],
["stars", 5],
# ...
["in", 2]
]
#longest_tokens
Returns a hash where each key-value pair is a token and its length.
counter.longest_tokens
{ "looking" => 7 }
#token_density([ precision: 2 ])
Returns a sorted (unstable) two-dimentional array where each element contains a token and its density as a float, rounded to a precision of two. The array is sorted by density in descending order. It accepts a precision
argument, which must be a float.
counter.token_density
[
["are", 0.13],
["the", 0.13],
["but", 0.07 ],
# ...
["we", 0.07 ]
]
#char_count
Returns the char count of tokens.
counter.char_count #=> 76
#average_chars_per_token([ precision: 2 ])
Returns the average char count per token rounded to two decimal places. Accepts a precision argument which defaults to two. Precision must be a float.
counter.average_chars_per_token #=> 4
#uniq_token_count
Returns the number of unique tokens.
counter.uniq_token_count #=> 13
You can exclude anything you want from the input by passing the exclude
option. The exclude option accepts a variety of filters and is extremely flexible.
:odd?
.tokeniser =
WordsCounted::Tokeniser.new(
"Magnificent! That was magnificent, Trevor."
)
# Using a string
tokeniser.tokenise(exclude: "was magnificent")
# => ["that", "trevor"]
# Using a regular expression
tokeniser.tokenise(exclude: /trevor/)
# => ["magnificent", "that", "was", "magnificent"]
# Using a lambda
tokeniser.tokenise(exclude: ->(t) { t.length < 4 })
# => ["magnificent", "that", "magnificent", "trevor"]
# Using symbol
tokeniser = WordsCounted::Tokeniser.new("Hello! محمد")
tokeniser.tokenise(exclude: :ascii_only?)
# => ["محمد"]
# Using an array
tokeniser = WordsCounted::Tokeniser.new(
"Hello! اسماءنا هي محمد، كارولينا، سامي، وداني"
)
tokeniser.tokenise(
exclude: [:ascii_only?, /محمد/, ->(t) { t.length > 6}, "و"]
)
# => ["هي", "سامي", "وداني"]
The default regexp accounts for letters, hyphenated tokens, and apostrophes. This means twenty-one is treated as one token. So is Mohamad's.
/[\p{Alpha}\-']+/
You can pass your own criteria as a Ruby regular expression to split your string as desired.
For example, if you wanted to include numbers, you can override the regular expression:
counter = WordsCounted.count("Numbers 1, 2, and 3", pattern: /[\p{Alnum}\-']+/)
counter.tokens
#=> ["numbers", "1", "2", "and", "3"]
Use the from_file
method to open files. from_file
accepts the same options as .count
. The file path can be a URL.
counter = WordsCounted.from_file("url/or/path/to/file.text")
A hyphen used in leu of an em or en dash will form part of the token. This affects the tokeniser algorithm.
counter = WordsCounted.count("How do you do?-you are well, I see.")
counter.token_frequency
[
["do", 2],
["how", 1],
["you", 1],
["-you", 1], # WTF, mate!
["are", 1],
# ...
]
In this example -you
and you
are separate tokens. Also, the tokeniser does not include numbers by default. Remember that you can pass your own regular expression if the default behaviour does not fit your needs.
The program will normalise (downcase) all incoming strings for consistency and filters.
def self.from_url
# open url and send string here after removing html
end
Are you using WordsCounted to do something interesting? Please tell me about it.
Visit this website for one example of what you can do with WordsCounted.
Contributors
See contributors.
git checkout -b my-new-feature
)git commit -am 'Add some feature'
)git push origin my-new-feature
)Author: Abitdodgy
Source Code: https://github.com/abitdodgy/words_counted
License: MIT license
1659601560
We are all in the gutter, but some of us are looking at the stars.
-- Oscar Wilde
WordsCounted is a Ruby NLP (natural language processor). WordsCounted lets you implement powerful tokensation strategies with a very flexible tokeniser class.
Are you using WordsCounted to do something interesting? Please tell me about it.
Visit this website for one example of what you can do with WordsCounted.
["Bayrūt"]
and not ["Bayr", "ū", "t"]
, for example.Add this line to your application's Gemfile:
gem 'words_counted'
And then execute:
$ bundle
Or install it yourself as:
$ gem install words_counted
Pass in a string or a file path, and an optional filter and/or regexp.
counter = WordsCounted.count(
"We are all in the gutter, but some of us are looking at the stars."
)
# Using a file
counter = WordsCounted.from_file("path/or/url/to/my/file.txt")
.count
and .from_file
are convenience methods that take an input, tokenise it, and return an instance of WordsCounted::Counter
initialized with the tokens. The WordsCounted::Tokeniser
and WordsCounted::Counter
classes can be used alone, however.
WordsCounted.count(input, options = {})
Tokenises input and initializes a WordsCounted::Counter
object with the resulting tokens.
counter = WordsCounted.count("Hello Beirut!")
Accepts two options: exclude
and regexp
. See Excluding tokens from the analyser and Passing in a custom regexp respectively.
WordsCounted.from_file(path, options = {})
Reads and tokenises a file, and initializes a WordsCounted::Counter
object with the resulting tokens.
counter = WordsCounted.from_file("hello_beirut.txt")
Accepts the same options as .count
.
The tokeniser allows you to tokenise text in a variety of ways. You can pass in your own rules for tokenisation, and apply a powerful filter with any combination of rules as long as they can boil down into a lambda.
Out of the box the tokeniser includes only alpha chars. Hyphenated tokens and tokens with apostrophes are considered a single token.
#tokenise([pattern: TOKEN_REGEXP, exclude: nil])
tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise
# With `exclude`
tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise(exclude: "hello")
# With `pattern`
tokeniser = WordsCounted::Tokeniser.new("I <3 Beirut!").tokenise(pattern: /[a-z]/i)
See Excluding tokens from the analyser and Passing in a custom regexp for more information.
The WordsCounted::Counter
class allows you to collect various statistics from an array of tokens.
#token_count
Returns the token count of a given string.
counter.token_count #=> 15
#token_frequency
Returns a sorted (unstable) two-dimensional array where each element is a token and its frequency. The array is sorted by frequency in descending order.
counter.token_frequency
[
["the", 2],
["are", 2],
["we", 1],
# ...
["all", 1]
]
#most_frequent_tokens
Returns a hash where each key-value pair is a token and its frequency.
counter.most_frequent_tokens
{ "are" => 2, "the" => 2 }
#token_lengths
Returns a sorted (unstable) two-dimentional array where each element contains a token and its length. The array is sorted by length in descending order.
counter.token_lengths
[
["looking", 7],
["gutter", 6],
["stars", 5],
# ...
["in", 2]
]
#longest_tokens
Returns a hash where each key-value pair is a token and its length.
counter.longest_tokens
{ "looking" => 7 }
#token_density([ precision: 2 ])
Returns a sorted (unstable) two-dimentional array where each element contains a token and its density as a float, rounded to a precision of two. The array is sorted by density in descending order. It accepts a precision
argument, which must be a float.
counter.token_density
[
["are", 0.13],
["the", 0.13],
["but", 0.07 ],
# ...
["we", 0.07 ]
]
#char_count
Returns the char count of tokens.
counter.char_count #=> 76
#average_chars_per_token([ precision: 2 ])
Returns the average char count per token rounded to two decimal places. Accepts a precision argument which defaults to two. Precision must be a float.
counter.average_chars_per_token #=> 4
#uniq_token_count
Returns the number of unique tokens.
counter.uniq_token_count #=> 13
You can exclude anything you want from the input by passing the exclude
option. The exclude option accepts a variety of filters and is extremely flexible.
:odd?
.tokeniser =
WordsCounted::Tokeniser.new(
"Magnificent! That was magnificent, Trevor."
)
# Using a string
tokeniser.tokenise(exclude: "was magnificent")
# => ["that", "trevor"]
# Using a regular expression
tokeniser.tokenise(exclude: /trevor/)
# => ["magnificent", "that", "was", "magnificent"]
# Using a lambda
tokeniser.tokenise(exclude: ->(t) { t.length < 4 })
# => ["magnificent", "that", "magnificent", "trevor"]
# Using symbol
tokeniser = WordsCounted::Tokeniser.new("Hello! محمد")
tokeniser.tokenise(exclude: :ascii_only?)
# => ["محمد"]
# Using an array
tokeniser = WordsCounted::Tokeniser.new(
"Hello! اسماءنا هي محمد، كارولينا، سامي، وداني"
)
tokeniser.tokenise(
exclude: [:ascii_only?, /محمد/, ->(t) { t.length > 6}, "و"]
)
# => ["هي", "سامي", "وداني"]
The default regexp accounts for letters, hyphenated tokens, and apostrophes. This means twenty-one is treated as one token. So is Mohamad's.
/[\p{Alpha}\-']+/
You can pass your own criteria as a Ruby regular expression to split your string as desired.
For example, if you wanted to include numbers, you can override the regular expression:
counter = WordsCounted.count("Numbers 1, 2, and 3", pattern: /[\p{Alnum}\-']+/)
counter.tokens
#=> ["numbers", "1", "2", "and", "3"]
Use the from_file
method to open files. from_file
accepts the same options as .count
. The file path can be a URL.
counter = WordsCounted.from_file("url/or/path/to/file.text")
A hyphen used in leu of an em or en dash will form part of the token. This affects the tokeniser algorithm.
counter = WordsCounted.count("How do you do?-you are well, I see.")
counter.token_frequency
[
["do", 2],
["how", 1],
["you", 1],
["-you", 1], # WTF, mate!
["are", 1],
# ...
]
In this example -you
and you
are separate tokens. Also, the tokeniser does not include numbers by default. Remember that you can pass your own regular expression if the default behaviour does not fit your needs.
The program will normalise (downcase) all incoming strings for consistency and filters.
def self.from_url
# open url and send string here after removing html
end
See contributors.
git checkout -b my-new-feature
)git commit -am 'Add some feature'
)git push origin my-new-feature
)Author: abitdodgy
Source code: https://github.com/abitdodgy/words_counted
License: MIT license
#ruby #ruby-on-rails
1622197808
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 @ https://bit.ly/3oFbJoJ
#create a defi token like safemoon #defi token like safemoon #safemoon token #safemoon token clone #defi token