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👾 𝗺𝘆 𝗰𝗹𝗶 𝘁𝗼𝗼𝗹 𝘁𝗼 𝗴𝗲𝘁 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗮𝗿𝗯𝗶𝘁𝗿𝗮𝗴𝗲 𝗳𝗼𝗿 𝗴𝗶𝘃𝗲𝗻 𝘁𝗼𝗸𝗲𝗻𝘀/𝗲𝘅𝗰𝗵𝗮𝗻𝗴𝗲 𝗽𝗼𝗼𝗹𝘀

⛓💰 bdex: an amm-toolkit



tl; dr

💰 my package and CLI tool to extract data and arbitrage for specified tokens/exchange pools.

📚 for more details, check my mirror post about this tool.






available token pairs:

  • WETH/DAI

available exchanges:




installation

Add your Alchemy API key and endpoint to a file named .env:

cp .env_example .env
vim .env

Create a virtual environment:

virtualenv venv
source venv/bin/activate

Install dependencies:

make install_deps

Install the CLI:

make install



running

You can run the CLI with:

bdex

Screen Shot 2022-04-04 at 1 35 46 AM


checking the latest block

We leverage Alchemy's API endpoint eth_blockNumber_hex to get the latest block:

bdex -c

Screen Shot 2022-03-31 at 1 15 25 PM



💡 The block number can be checked against ETHstat.


Screen Shot 2022-03-31 at 1 15 19 PM


💡 We are crafting the checksum address string by hand without directly Keccak-256 hashing the methods and parameters.


getting the token balance for an exchange

We leverage Alchemy API endpoint eth_call to retrieve the current token balance for a specific exchange:

bdex -b TOKEN EXCHANGE

Screen Shot 2022-03-31 at 10 26 21 PM


getting all token balances for all the exchanges

We loop over the previous method for a list of tokens and exchanges:

bdex -a

Screen Shot 2022-03-31 at 10 26 51 PM


[extra] getting all token balances for all exchanges with Python's web3 library

To be able to compare our results from the previous steps, we implemented an alternative way to fetch pair balances utilizing the Python web3 library:

bdex -w

Screen Shot 2022-03-31 at 10 27 25 PM



💡 For this library, it's necessary to supply the contracts' ABI (in our case, for DAI and WETH, located at ./docs)

💡 A third option to verify token balances is through Etherscan tokenholdings dashboard.


getting trading prices for all the exchanges

To get the current price for WETH/DAI in all exchanges (e.g., as shown in the projects' dashboards), run:

bdex -p QUANTITY TOKEN1 TOKEN2

Quote for trading 1 WETH:


Screen Shot 2022-04-03 at 8 27 55 PM


Quote for trading 10 WETH:


Screen Shot 2022-04-03 at 8 28 20 PM


Quote for trading 100 WETH:


Screen Shot 2022-04-03 at 8 28 46 PM


how the price is calculated

An AMM replaces the buy and sell orders in an order book market with a liquidity pool of two assets, both valued relative to each other. As one asset is traded for the other, the relative prices of the two assets shift, and the new market rate for both is determined.

The constant product is:

token_a_pool_size * token_b_pool_size = constant_product

All the exchanges are forks from UniswapV2, so they all use the same price formula for trading:

market_price_token1 = token2_balance / token1_balance

For example, in a pool with 2,000,000 DAI and 1,000 WETH, the constant product is 2,000,000,000 and the market price for WETH is $2,000.


Buy price (e.g., buying WETH in a WETH/DAI pool)

To find the buy price for a certain quantity, first, we calculate how much WETH needs to remain in balance to keep the constant product unchanged:

token1_balance_buy = constant_product / (token2_balance + quantity)

Then we calculate how much WETH goes out to keep this constant:

 t1_amount_out_buy = token1_balance - token1_balance_buy

The buy price to reflect this ratio is:

buy_price = quantity / t1_amount_out_buy

sell price (e.g., selling WETH in a WETH/DAI pool)

To find how much we can sell a certain quantity of WETH for DAI, first, we calculate the ratio of DAI in the new pool, as we add WETH:

token2_balance_buy = constant_product / (token1_balance + quantity)

We then calculate how much DAI will go out:

t2_amount_out_buy = token2_balance + token2_balance_buy

We calculate the DAI balance reflected with the income WETH:

token1_balance_sell = constant_product / (token2_balance - quantity)

And what's the proportion of WETH in the new balance:

t1_amount_in_sell = token1_balance + token1_balance_sell

We can now calculate the sell price to reflect the balance change, keeping the constant:

sell_price = t2_amount_out_buy / t1_amount_in_sell


getting arbitrages

Run an algorithm to search for arbitrage in the supported exchanges for a certain buy quantity:

bdex -x QUANTITY

Arbitrage opportunities for 10 WETH:


Screen Shot 2022-04-04 at 6 02 09 PM


Arbitrage opportunities for 1 WETH:


Screen Shot 2022-04-04 at 6 02 31 PM


Arbitrage opportunities for 0.01 WETH:


Screen Shot 2022-04-04 at 6 03 20 PM




running arbitrage algorithm in a loop

To run the arbitrage algorithm for a certain amount of minutes:

bdex -r MIN

Screen Shot 2022-04-04 at 1 05 07 PM



Results are saved into results/<arbitrage_TIMESTAMP>.txt.


Here is a sample of the results running this algorithm for 100 minutes for trading 1 WETH:

{'buy_exchange': 'SUSHISWAP', 'sell_exchange': 'UNISWAP', 'arbitrage': '7.01', 'buy_price': 3475.14, 'sell_price': 3482.15}
{'buy_exchange': 'SUSHISWAP', 'sell_exchange': 'SHEBASWAP', 'arbitrage': '4.27', 'buy_price': 3475.14, 'sell_price': 3479.41}
{'buy_exchange': 'SHEBASWAP', 'sell_exchange': 'UNISWAP', 'arbitrage': '2.06', 'buy_price': 3480.09, 'sell_price': 3482.15}
{'buy_exchange': 'CROSWAP', 'sell_exchange': 'UNISWAP', 'arbitrage': '13.06', 'buy_price': 3469.09, 'sell_price': 3482.15}
{'buy_exchange': 'CROSWAP', 'sell_exchange': 'SUSHISWAP', 'arbitrage': '5.79', 'buy_price': 3469.09, 'sell_price': 3474.88}
{'buy_exchange': 'CROSWAP', 'sell_exchange': 'SHEBASWAP', 'arbitrage': '10.32', 'buy_price': 3469.09, 'sell_price': 3479.41}
...

running arbitrage algorithm in a loop in a Docker container

To run the algorithm in a separated container, first install Docker, then build the Docker image:

docker build -t bdex .

Finally, run the container (in a separate terminal tab):

docker run -v $(pwd):/results -it bdex sleep infinity

Results are available at results/<arbitrage_TIMESTAMP>.txt.

💡 You can inspect your container at any time with these commands:

docker ps
docker exec -it <container_id> /bin/bash
docker inspect bdex

Cleaning up:

docker volumes prune



development

Install dependencies (in a virtual env):

make install

Useful commands:

make lint
make test

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25

shell-toolkit

👾 𝗱𝗼𝘁𝗳𝗶𝗹𝗲𝘀, 𝘀𝗵𝗲𝗹𝗹 𝘀𝗰𝗿𝗶𝗽𝘁𝘀, 𝘀𝗼𝗺𝗲 𝘂𝘀𝗲𝗳𝘂𝗹 𝘀𝗰𝗿𝗶𝗽𝘁𝘀
Shell
9
star
26

adv-optimization-physics-py

👾 𝗻𝘂𝗺𝗲𝗿𝗶𝗰𝗮𝗹 𝗺𝗲𝘁𝗵𝗼𝗱𝘀 𝗳𝗼𝗿 𝗽𝗵𝘆𝘀𝗶𝗰𝘀: 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗶𝗼𝗻, 𝗼𝗿𝗱𝗶𝗻𝗮𝗿𝘆, 𝗮𝗱𝘃𝗲𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗵𝘆𝗽𝗲𝗿𝗯𝗼𝗹𝗶𝗰 𝗽𝗮𝗿𝘁𝗶𝗮𝗹 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝗲𝗾𝘀
Python
9
star
27

coinmixer-py

👾 𝗯𝗮𝗰𝗸-𝗼𝗳-𝘁𝗵𝗲-𝗲𝗻𝘃𝗲𝗹𝗼𝗽𝗲 𝗳𝗼𝗿 𝗮 𝗰𝗿𝘆𝗽𝘁𝗼 𝗰𝗼𝗶𝗻 𝗺𝗶𝘅𝗲𝗿
Python
8
star
28

flask

👾 𝗙𝗹𝗮𝘀𝗸: 𝗯𝗼𝗶𝗹𝗲𝗿𝗽𝗹𝗮𝘁𝗲𝘀 𝗮𝗻𝗱 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀.
Python
8
star
29

web3-toolkit-go

👾 𝗽𝗹𝗮𝘆𝗶𝗻𝗴 𝗽𝘃𝗽 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗲𝘁𝗮𝘄𝗲𝗯 - 𝗴𝗼 𝗲𝗱𝗶𝘁𝗶𝗼𝗻
Go
8
star
30

arkit

👾 𝗔𝗥𝗸𝗶𝘁 𝗮𝗽𝗽 𝗱𝗲𝗺𝗼.
Swift
8
star
31

ml-deep-learning-agents-py

👾 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗱𝗲𝗲𝗽 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀, 𝗽𝗹𝘂𝘀 𝘀𝗼𝗺𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
Python
8
star
32

singularity.sh

👾 𝗼𝗹𝗱 𝗯𝗹𝗼𝗴 𝗼𝗻 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗵𝗮𝗰𝗸𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴
HTML
8
star
33

interstellar

👾 𝗜𝗻𝘁𝗲𝗿𝘀𝘁𝗲𝗹𝗹𝗮𝗿 𝗗𝘂𝘀𝘁 𝗱𝗮𝘁𝗮 𝗿𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗼𝗯𝘁𝗮𝗶𝗻𝗲𝗱 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗦𝘁𝗼𝗻𝘆 𝗕𝗿𝗼𝗼𝗸 𝗠𝘁. 𝗧𝗲𝗹𝗲𝘀𝗰𝗼𝗽𝗲
IDL
8
star
34

robotics

👾 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗼𝗻 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝟯𝗗 𝗽𝗵𝗼𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝘆
MATLAB
8
star
35

ml-graph-network-analyser

👾 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻/𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗳𝗼𝗿 𝗴𝗿𝗮𝗽𝗵 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗳𝗿𝗼𝗺 𝗽𝘂𝗯𝗹𝗶𝗰 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀, 𝘂𝘀𝗶𝗻𝗴 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗫
Python
8
star
36

antisocial

👾 𝗧𝘄𝗶𝘁𝘁𝗲𝗿-𝗹𝗶𝗸𝗲 (𝗮𝗻𝘁𝗶) 𝘀𝗼𝗰𝗶𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗶𝗻 𝗙𝗹𝗮𝘀𝗸, 𝘄𝗵𝗲𝗿𝗲 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗰𝗿𝗲𝗮𝘁𝗲 𝗮𝘀 𝗺𝗮𝗻𝘆 𝗶𝗺𝗮𝗴𝗶𝗻𝗮𝗿𝘆 𝗳𝗿𝗶𝗲𝗻𝗱𝘀 𝗮𝘀 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝗮𝗻𝗱 𝗽𝗼𝘀𝘁
HTML
8
star
37

ethernaut-systematic-solutions-foundry-sol

👾 𝗺𝘆 𝗺𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆 𝗳𝗼𝗿 𝗱𝗲𝘁𝗮𝗶𝗹𝗲𝗱 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗳𝗼𝘂𝗻𝗱𝗿𝘆 𝗯𝗼𝗶𝗹𝗲𝗿𝗽𝗹𝗮𝘁𝗲𝘀 𝗳𝗼𝗿 𝗲𝗮𝗰𝗵 𝗲𝘁𝗵𝗲𝗿𝗻𝗮𝘂𝘁 𝗹𝗲𝘃𝗲𝗹
Solidity
8
star
38

web3-toolkit-py

👾 𝘄𝗲𝗯𝟯 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗸𝗶𝘁 - 𝗽𝘆𝘁𝗵𝗼𝗻 𝗲𝗱𝗶𝘁𝗶𝗼𝗻
Python
7
star
39

ml-tensorflower

👾 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵, 𝗮𝗻𝗱 𝘀𝗼𝗺𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
Jupyter Notebook
7
star
40

cuda-toolkit

👾 𝗖𝗨𝗗𝗔 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝗳𝗼𝗿 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗯𝗹𝗲𝗺𝘀
Cuda
7
star
41

ml-netclean

👾 𝗖𝗹𝗲𝗮𝗻𝘀𝗲 𝗼𝘂𝘁𝗽𝘂𝘁 𝗳𝗶𝗹𝗲𝘀 𝗳𝗿𝗼𝗺 𝗡𝗲𝘁𝗔𝗻𝗮-𝗖𝗼𝗺𝗽𝗹𝗲𝘅-𝗡𝗲𝘁𝘄𝗼𝗿𝗸-𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀.
Python
7
star
42

astrophysics-mfits-py

👾 𝗣𝗮𝗰𝗸𝗮𝗴𝗲 𝘁𝗼 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲 𝗳𝗶𝘃𝗲 𝗱𝗶𝘀𝘁𝗶𝗻𝗰𝘁 𝗳𝗶𝘁𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗫-𝗿𝗮𝘆 𝗮𝘁𝗺𝗼𝘀𝗽𝗵𝗲𝗿𝗲 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀.
Python
7
star
43

exolife

👾 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝗲𝘅𝗼𝗹𝗶𝗳𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗴𝗼𝗮𝗹𝘀 𝗳𝗼𝗿 𝗳𝘂𝘁𝘂𝗿𝗲 𝘀𝗽𝗮𝗰𝗲 𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀 - 𝗶𝗺𝗮𝗴𝗲𝘀 𝘁𝗮𝗸𝗲𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗺𝗼𝗼𝗻 𝗮𝘁 𝘁𝗵𝗲 𝗠𝘁. 𝗦𝘁𝗼𝗻𝘆 𝗕𝗿𝗼𝗼𝗸
TeX
7
star
44

hugo-go

👾 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘂𝘀𝗶𝗻𝗴 𝗛𝘂𝗴𝗼 + 𝗚𝗼𝗹𝗮𝗻𝗴 𝗳𝗼𝗿 𝗮𝗽𝗽𝘀
HTML
7
star
45

solar

👾 𝗜𝗗𝗟 𝗦𝗼𝘂𝗿𝗰𝗲 𝗰𝗼𝗱𝗲 𝗳𝗼𝗿 𝗱𝗮𝘁𝗮 𝗿𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗶𝗻𝘁𝗲𝗿𝗳𝗲𝗿𝗼𝗺𝗲𝘁𝗿𝗶𝗰 𝗿𝗮𝗱𝗶𝗼 𝗱𝗮𝘁𝗮 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗦𝘂𝗻 𝘁𝗼 𝗯𝗲 𝗮𝗯𝗹𝗲 𝘁𝗼 𝗼𝗯𝘁𝗮𝗶𝗻 𝗶𝘁𝘀 𝗮𝗻𝗴𝘂𝗹𝗮𝗿 𝗱𝗶𝗮𝗺𝗲𝘁𝗲𝗿)
Mathematica
7
star
46

tetris-java

👾 𝘁𝗲𝘁𝗿𝗶𝘀 𝗴𝗮𝗺𝗲 𝗶𝗻 𝗷𝗮𝘃𝗮
Java
6
star
47

latex

👾 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝗶𝗻 𝗟𝗮𝗧𝗲𝘅: 𝗮𝗿𝘁𝗶𝗰𝗹𝗲, 𝗯𝗼𝗼𝗸, 𝗽𝗼𝘀𝘁𝗲𝗿, 𝗵𝗼𝗺𝗲𝘄𝗼𝗿𝗸, 𝗿𝗲𝘀𝘂𝗺𝗲, 𝘁𝗮𝗹𝗸𝘀, 𝗣𝗵𝗗 𝗱𝗶𝘀𝘀𝗲𝗿𝘁𝗮𝘁𝗶𝗼𝗻, 𝗲𝘁𝗰
TeX
6
star
48

aws-pipeline-py

👾 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝘄𝗶𝘁𝗵 𝗔𝗪𝗦 𝗹𝗮𝗺𝗯𝗱𝗮 + 𝘀𝗾𝘀 + 𝘀𝗻𝘀 + 𝘀𝟯
Python
6
star
49

qtensorflow

👾 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝘁𝗲𝗻𝘀𝗼𝗿𝗳𝗹𝗼𝘄 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗶𝗻 𝗸𝟴𝘀
Jupyter Notebook
6
star
50

neo-cypherpunk-toolkit

👾 𝘁𝗵𝗲 𝗰𝘆𝗽𝗵𝗲𝗿𝗽𝘂𝗻𝗸 𝗳𝘂𝘁𝘂𝗿𝗲 𝗶𝘀 𝗵𝗲𝗿𝗲; 𝘄𝗵𝗮𝘁 𝗱𝗼 𝘆𝗼𝘂 𝗱𝗼 𝗻𝗼𝘄? 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗱𝗼𝗲𝘀 𝗻𝗼𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝗲 𝗹𝗶𝗸𝗲 𝘁𝗵𝗲 𝗽𝗮𝘀𝘁; 𝘆𝗼𝘂 𝗮𝗿𝗲 𝘀𝘁𝗿𝗼𝗻𝗴 𝗲𝗻𝗼𝘂𝗴𝗵 𝘁𝗼 𝗰𝗵𝗮𝗻𝗴𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆; 𝖕𝖗𝖔𝖈𝖊𝖊𝖉 𝖆𝖙 𝖞𝖔𝖚𝖗 𝖔𝖜𝖓 𝖗𝖎𝖘𝖐
6
star
51

lamp-php

👾 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝗳𝗼𝗿 𝗮 𝗟𝗔𝗠𝗣 𝘀𝘁𝗮𝗰𝗸, 𝘂𝘀𝗶𝗻𝗴 𝗖𝗼𝗱𝗲𝗜𝗴𝗻𝗶𝘁𝗲𝗿
PHP
5
star
52

games-js

👾 𝗴𝗮𝗺𝗲𝘀 𝗶𝗻 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁
JavaScript
5
star
53

eth-nft-dapp-js

👾 𝗺𝗶𝗻𝘁𝗶𝗻𝗴 𝗱𝗮𝗽𝗽 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗲𝗿 𝗖𝗮𝗿𝗱𝘀 𝗡𝗙𝗧 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗳𝗶𝗹𝗺𝗺𝗮𝗸𝗲𝗿 𝗱𝗮𝗼
TypeScript
5
star
54

blockchain-infrastructure-design

👾 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗮𝗻𝗱 𝗠𝗩𝗣 𝘀𝗼𝘂𝗿𝗰𝗲 𝗰𝗼𝗱𝗲, 𝘀𝘂𝗰𝗵 𝗮𝘀 𝗮 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗲𝘃𝗲𝗻𝘁 𝘀𝗰𝗮𝗻𝗻𝗲𝗿𝘀, 𝗳𝗼𝗿 𝗼𝗻-𝗰𝗵𝗮𝗶𝗻 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀, 𝗵𝗳𝘁, 𝗺𝗹...
Python
4
star
55

cyph3rs-vue-js

👾 𝘁𝗵𝗲 𝗺𝗮𝗿𝗸𝗲𝘁𝗽𝗹𝗮𝗰𝗲 𝗳𝗼𝗿 𝗺𝗲𝘃 𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝗮𝗻𝗱 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀
Vue
3
star
56

.github

👾 "the feeling of awed wonder that science can give us is one of the highest experiences of which the human psyche is capable. It is a deep aesthetic passion to rank with the finest that music and poetry can deliver." - richard dawkins
1
star
57

earth-location-api-py

👾 𝗲𝗮𝗿𝘁𝗵 𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝗽𝗶
Python
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58

bt3gl-theoretical-physics-calculations

👾 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗮 𝗽𝗵𝗱 𝗶𝗻 𝘁𝗵𝗲𝗼𝗿𝗲𝘁𝗶𝗰𝗮𝗹 𝗽𝗵𝘆𝘀𝗶𝗰𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲 - 𝘁𝗵𝗶𝘀 𝗿𝗲𝗽𝗼 𝗵𝗼𝗻𝗼𝗿𝘀 𝗯𝘁𝟯𝗴𝗹'𝘀 𝗴𝗲𝗻𝗶𝗮𝗹𝗶𝘁𝘆, 𝗴𝗮𝘁𝗵𝗲𝗿𝗶𝗻𝗴 𝗵𝗲𝗿 𝗻𝗼𝘁𝗲𝘀 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗵𝗲 𝘆𝗲𝗮𝗿𝘀 (𝗯𝘆 𝗵𝗲𝗿 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀)
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