Defi - Decentralised Finance

Decentralized Finance or, DeFi, refers to various decentralized protocols built on the blockchain network (mainly the Ethereum network) which are aimed to build an open financial system with transparency, accessibility and inclusiveness.

The components of DeFi include smart contracts, protocols, decentralized applications, digital currencies, and more.

DeFi is also known as “money lego”, it has entirely license-free access, financial applications can be built, connected and used without restrictions.

DeFi aims to use smart contracts to replace traditional institutions in Financial Field, allowing users to enjoy financial services at a lower cost, and improve the operational efficiency of the entire financial system, reduce operational costs. At the same time, DeFi should create a global open borderless financial system, so that everyone can trade freely and transparently.

 DeFi projects have two main advantages over traditional centralized financial systems：

 1. Individuals with demand for asset management need no longer trust any intermediaries to enjoy a variety of financial services, the new trust is built on the code.

 2. All protocols are an open source, where everyone can work together to create new financial products and develop financial innovations under the blockchain network.

 At present, DeFi services include items such as savings and checking accounts, loans, asset trading, insurance, derivatives, market predicting, payment platforms. DeFi makes us believe that in the future, under the new financial order created by DeFi, more revolutions and innovations can emerge beyond our imagination.

Courtesy - Huobi.

 

Comments

Post a Comment

Popular posts from this blog

Resources and Links to learn Python for Data Science.

Python Basics: Official Python Documentation: Python.org Codecademy Python Course: Codecademy Python Libraries for Data Science: NumPy: numpy.org Pandas: pandas.pydata.org Matplotlib: matplotlib.org Data Visualization: Seaborn: seaborn.pydata.org Plotly: plotly.com Data Visualization with Matplotlib Tutorial: matplotlib.org/3.3.4/tutorials/index.html Statistical Analysis: SciPy: scipy.org Statsmodels: statsmodels.org Statistics for Data Science with Python: realpython.com Machine Learning: Scikit-learn: scikit-learn.org TensorFlow: tensorflow.org PyTorch: pytorch.org Advanced Topics in Data Science: Natural Language Processing with NLTK: nltk.org Time Series Analysis with Python: statsmodels.org/dev/examples/index.html#time-series-analysis Deep Learning with TensorFlow: tensorflow.org/tutorials Deep Learning with PyTorch: pytorch.org/tutorials Data Science Communities and Platforms: Kaggle (Data Science Competitions and Datasets): kaggle.com Data Science Stack Exchange: datascience.sta
Read more

Electroculture Benefits & Uses. Part 2

Let's dive deeper into electroculture and explore some specific techniques and applications: Electrostatic Fields : One aspect of electroculture involves the use of electrostatic fields. Plants naturally have a weak positive electrical charge on their surface, and applying a negative charge to the soil can create an electrostatic field. This field is believed to enhance root growth and nutrient uptake. It can also help repel certain pests, reduce soil compaction, and increase water infiltration. Electromagnetic Fields : Another approach in electroculture is the application of low-frequency electromagnetic fields. These fields can be generated through various means, such as buried wires, antennas, or specialized devices. When plants are exposed to these fields, it can stimulate cellular activities and biochemical reactions, leading to increased growth and yield. Electromagnetic fields have been shown to influence the expression of specific genes related to plant growth and stress re
Read more

10-week plan to learn Python for data science and data analysis, along with hands-on projects for each week.

Week 1: Introduction to Python and Data Manipulation Resources: Python Crash Course by Eric Matthes: book Codecademy Python Course: codecademy.com Hands-on Project: Perform basic Python exercises to get familiar with the language and practice data manipulation using Python lists. Week 2: Data Analysis with Pandas Resources: Pandas User Guide: pandas.pydata.org "Python for Data Analysis" by Wes McKinney: book Hands-on Project: Analyze a real-world dataset using Pandas. Practice loading data, performing data cleaning, filtering, and basic exploratory data analysis (EDA). Week 3: Data Visualization with Matplotlib and Seaborn Resources: Matplotlib Documentation: matplotlib.org Seaborn Documentation: seaborn.pydata.org Hands-on Project: Create various visualizations (line plots, bar charts, scatter plots, etc.) using Matplotlib and Seaborn with a dataset of your choice. Week 4: Statistical Analysis with SciPy and Statsmodels Resources: SciPy Documentation: scipy.org Statsmodels D
Read more